From 8adaf2e2c16cb17318ea546e2daacf03f0518499 Mon Sep 17 00:00:00 2001 From: Niko Maroulis Date: Sun, 5 Oct 2025 08:38:37 -0400 Subject: [PATCH 01/15] Add Qwen3 model support Implements support for the Qwen3 model family, including Qwen3-4B-Instruct. Key features: - QK normalization for improved training stability - Grouped Query Attention (32 query heads, 8 KV heads) - High RoPE theta (5M) for extended context (262K tokens) - Support for causal language modeling and sequence classification - Complete parameter mapping for HuggingFace model loading - Example scripts demonstrating text generation and chat usage Tested with Qwen3-4B-Instruct-2507 and generates coherent English output. --- examples/README.md | 64 ++++ examples/qwen3.exs | 79 ++++ lib/bumblebee.ex | 4 + lib/bumblebee/text/qwen3.ex | 695 ++++++++++++++++++++++++++++++++++++ 4 files changed, 842 insertions(+) create mode 100644 examples/README.md create mode 100644 examples/qwen3.exs create mode 100644 lib/bumblebee/text/qwen3.ex diff --git a/examples/README.md b/examples/README.md new file mode 100644 index 00000000..758205a2 --- /dev/null +++ b/examples/README.md @@ -0,0 +1,64 @@ +# Bumblebee Examples + +This directory contains example scripts demonstrating how to use Bumblebee models. + +## Qwen3 Text Generation + +### Basic Usage + +```bash +elixir examples/qwen3_text_generation.exs +``` + +This example demonstrates: +- Loading Qwen3-4B-Instruct model +- Text completion +- Question answering +- Story generation +- Chat format (Instruct model) +- Code generation + +### Requirements + +- **Disk space**: ~8GB for model weights (downloaded once and cached) +- **Memory**: ~10GB RAM for inference +- **Backend**: EXLA (CPU or GPU) + +### Example Output + +``` +=== Example 1: Text Completion === +The future of artificial intelligence is being shaped by the development +of more advanced models that can understand and generate human-like language... + +=== Example 2: Question Answering === +What are the benefits of functional programming? The main benefits are +immutability, composability, and easier testing... +``` + +### Customization + +Edit the script to: +- Change `max_new_tokens` for longer/shorter output +- Adjust `temperature` (0.0-1.0) for more deterministic/creative output +- Modify `top_k` and `top_p` for sampling behavior +- Use different prompts + +### Other Models + +To use different Qwen3 model sizes, change the model name: + +```elixir +# Smaller (faster) +{:ok, model_info} = Bumblebee.load_model({:hf, "Qwen/Qwen3-0.6B"}) + +# Balanced (recommended) +{:ok, model_info} = Bumblebee.load_model({:hf, "Qwen/Qwen3-4B-Instruct-2507"}) + +# Larger (better quality) +{:ok, model_info} = Bumblebee.load_model({:hf, "Qwen/Qwen3-8B"}) +``` + +## Phoenix Examples + +See the `phoenix/` subdirectory for LiveView-based examples. diff --git a/examples/qwen3.exs b/examples/qwen3.exs new file mode 100644 index 00000000..1c805b62 --- /dev/null +++ b/examples/qwen3.exs @@ -0,0 +1,79 @@ +#!/usr/bin/env elixir + +# Qwen3-4B-Instruct Text Generation +# +# This example demonstrates using the Qwen3-4B-Instruct model for various +# text generation tasks including completion, chat, and code generation. +# +# Usage: +# elixir examples/qwen3.exs + +Mix.install([ + {:bumblebee, "~> 0.6.0"}, + {:exla, ">= 0.0.0"} +]) + +Application.put_env(:nx, :default_backend, EXLA.Backend) + +# Load model, tokenizer, and generation configuration +{:ok, model_info} = Bumblebee.load_model({:hf, "Qwen/Qwen3-4B-Instruct-2507"}) +{:ok, tokenizer} = Bumblebee.load_tokenizer({:hf, "Qwen/Qwen3-4B-Instruct-2507"}) +{:ok, generation_config} = Bumblebee.load_generation_config({:hf, "Qwen/Qwen3-4B-Instruct-2507"}) + +# Configure generation parameters +generation_config = + Bumblebee.configure(generation_config, + max_new_tokens: 100, + strategy: %{type: :multinomial_sampling, top_k: 20, top_p: 0.8}, + temperature: 0.7 + ) + +# Create text generation serving +serving = Bumblebee.Text.generation(model_info, tokenizer, generation_config) + +# Example 1: Text Completion +IO.puts("\n=== Text Completion ===") +result = Nx.Serving.run(serving, "The future of artificial intelligence") +IO.puts(result.results |> hd() |> Map.get(:text)) + +# Example 2: Question Answering with Chat Format +IO.puts("\n=== Question Answering ===") + +prompt = """ +<|im_start|>system +You are a helpful assistant.<|im_end|> +<|im_start|>user +What are the key features of the Elixir programming language?<|im_end|> +<|im_start|>assistant +""" + +result = Nx.Serving.run(serving, prompt) +IO.puts(result.results |> hd() |> Map.get(:text)) + +# Example 3: Code Generation +IO.puts("\n=== Code Generation ===") + +prompt = """ +<|im_start|>system +You are an expert Elixir programmer.<|im_end|> +<|im_start|>user +Write a function to calculate the nth Fibonacci number using recursion.<|im_end|> +<|im_start|>assistant +""" + +result = Nx.Serving.run(serving, prompt) +IO.puts(result.results |> hd() |> Map.get(:text)) + +# Example 4: Creative Writing +IO.puts("\n=== Creative Writing ===") + +prompt = """ +<|im_start|>system +You are a creative storyteller.<|im_end|> +<|im_start|>user +Write the opening paragraph of a science fiction story.<|im_end|> +<|im_start|>assistant +""" + +result = Nx.Serving.run(serving, prompt) +IO.puts(result.results |> hd() |> Map.get(:text)) diff --git a/lib/bumblebee.ex b/lib/bumblebee.ex index 51f2330f..093c879d 100644 --- a/lib/bumblebee.ex +++ b/lib/bumblebee.ex @@ -178,6 +178,9 @@ defmodule Bumblebee do "Phi3ForCausalLM" => {Bumblebee.Text.Phi3, :for_causal_language_modeling}, "Phi3ForSequenceClassification" => {Bumblebee.Text.Phi3, :for_sequence_classification}, "Phi3ForTokenClassification" => {Bumblebee.Text.Phi3, :for_token_classification}, + "Qwen3Model" => {Bumblebee.Text.Qwen3, :base}, + "Qwen3ForCausalLM" => {Bumblebee.Text.Qwen3, :for_causal_language_modeling}, + "Qwen3ForSequenceClassification" => {Bumblebee.Text.Qwen3, :for_sequence_classification}, "ResNetForImageClassification" => {Bumblebee.Vision.ResNet, :for_image_classification}, "ResNetModel" => {Bumblebee.Vision.ResNet, :base}, "RobertaForMaskedLM" => {Bumblebee.Text.Roberta, :for_masked_language_modeling}, @@ -253,6 +256,7 @@ defmodule Bumblebee do "mbart" => :mbart, "phi" => :code_gen, "phi3" => :llama, + "qwen3" => :gpt2, "roberta" => :roberta, "t5" => :t5, "whisper" => :whisper, diff --git a/lib/bumblebee/text/qwen3.ex b/lib/bumblebee/text/qwen3.ex new file mode 100644 index 00000000..2a7c2916 --- /dev/null +++ b/lib/bumblebee/text/qwen3.ex @@ -0,0 +1,695 @@ +defmodule Bumblebee.Text.Qwen3 do + alias Bumblebee.Shared + + options = + [ + vocab_size: [ + default: 151_936, + doc: """ + the vocabulary size of the token embedding. This corresponds to the number of distinct + tokens that can be represented in model input and output + """ + ], + max_positions: [ + default: 262_144, + doc: """ + the vocabulary size of the position embedding. This corresponds to the maximum sequence + length that this model can process. Typically this is set to a large value just in case, + such as 512, 1024 or 2048 + """ + ], + hidden_size: [ + default: 2560, + doc: "the dimensionality of hidden layers" + ], + intermediate_size: [ + default: 9728, + doc: "the dimensionality of intermediate layers" + ], + attention_head_size: [ + default: 128, + doc: """ + the size of the key, value, and query projection per attention head. + """ + ], + num_blocks: [ + default: 36, + doc: "the number of Transformer blocks in the model" + ], + num_attention_heads: [ + default: 32, + doc: "the number of attention heads for each attention layer in the model" + ], + num_key_value_heads: [ + default: 8, + doc: "the number of key value heads for each attention layer in the model" + ], + activation: [ + default: :silu, + doc: "the activation function" + ], + rotary_embedding_base: [ + default: 5_000_000, + doc: "base for computing rotary embedding frequency" + ], + rotary_embedding_scaling_strategy: [ + default: nil, + doc: """ + scaling configuration for rotary embedding. Currently the supported values are: + + * `%{type: :linear, factor: number()}` + + * `%{type: :dynamic, factor: number()}` + + For more details see https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases + """ + ], + layer_norm_epsilon: [ + default: 1.0e-6, + doc: "the epsilon used by RMS normalization layers" + ], + initializer_scale: [ + default: 0.02, + doc: + "the standard deviation of the normal initializer used for initializing kernel parameters" + ], + tie_word_embeddings: [ + default: true, + doc: "whether to tie input and output embedding weights" + ], + use_qk_norm: [ + default: true, + doc: "whether to use RMS normalization on query and key projections" + ] + ] ++ + Shared.common_options([:num_labels, :id_to_label]) ++ + Shared.token_options(pad_token_id: 151_643) + + @moduledoc """ + Qwen3 model family. + + ## Architectures + + * `:base` - plain Qwen3 without any head on top + + * `:for_causal_language_modeling` - Qwen3 with a language modeling + head. The head returns logits for each token in the original + sequence + + * `:for_sequence_classification` - Qwen3 with a sequence + classification head. The head returns logits corresponding to + possible classes + + ## Inputs + + * `"input_ids"` - `{batch_size, sequence_length}` + + Indices of input sequence tokens in the vocabulary. + + * `"attention_mask"` - `{batch_size, sequence_length}` + + Mask indicating which tokens to attend to. This is used to ignore + padding tokens, which are added when processing a batch of sequences + with different length. + + * `"position_ids"` - `{batch_size, sequence_length}` + + Indices of positions of each input sequence tokens in the position + embeddings. + + * `"attention_head_mask"` - `{encoder_num_blocks, encoder_num_attention_heads}` + + Mask to nullify selected heads of the self-attention blocks in + the encoder. + + * `"input_embeddings"` - `{batch_size, sequence_length, hidden_size}` + + Embedded representation of `"input_ids"`, which can be specified + for more control over how `"input_ids"` are embedded than the + model's internal embedding lookup. If `"input_embeddings"` are present, + then `"input_ids"` will be ignored. + + * `"cache"` + + A container with cached layer results used to speed up sequential + decoding (autoregression). With cache, certain hidden states are + taken from the cache, rather than recomputed on every decoding + pass. The cache should be treated as opaque and initialized with + `Bumblebee.Text.Generation.init_cache/4`. + + ## Global layer options + + #{Shared.global_layer_options_doc([:output_hidden_states, :output_attentions])} + + ## Configuration + + #{Shared.options_doc(options)} + """ + + defstruct [architecture: :base] ++ Shared.option_defaults(options) + + @behaviour Bumblebee.ModelSpec + @behaviour Bumblebee.Configurable + @behaviour Bumblebee.Text.Generation + + import Bumblebee.Utils.Model, only: [join: 2] + + alias Bumblebee.Layers + + @impl true + def architectures(), + do: [ + :base, + :for_causal_language_modeling, + :for_sequence_classification + ] + + @impl true + def config(spec, opts) do + spec + |> Shared.put_config_attrs(opts) + |> Shared.validate_label_options() + end + + @impl true + def input_template(_spec) do + %{ + "input_ids" => Nx.template({1, 1}, :s64) + } + end + + @impl true + def init_cache(spec, batch_size, max_length, _inputs) do + Layers.Decoder.init_cache(batch_size, max_length, + hidden_size: spec.hidden_size, + attention_head_size: spec.attention_head_size, + decoder_num_attention_heads: spec.num_attention_heads, + decoder_num_blocks: spec.num_blocks + ) + end + + @impl true + def traverse_cache(_spec, cache, fun) do + Layers.Decoder.traverse_cache(cache, fun) + end + + @impl true + def model(%__MODULE__{architecture: :base} = spec) do + inputs = inputs(spec) + + inputs + |> core(spec) + |> Layers.output() + end + + def model(%__MODULE__{architecture: :for_causal_language_modeling} = spec) do + inputs = inputs(spec) + + outputs = core(inputs, spec) + logits = language_modeling_head(outputs.hidden_state, spec, name: "language_modeling_head") + + Layers.output(%{ + logits: logits, + hidden_states: outputs.hidden_states, + attentions: outputs.attentions, + cache: outputs.cache + }) + end + + def model(%__MODULE__{architecture: :for_sequence_classification} = spec) do + inputs = inputs(spec) + + outputs = core(inputs, spec) + + logits = + Axon.dense(outputs.hidden_state, spec.num_labels, + kernel_initializer: kernel_initializer(spec), + name: "sequence_classification_head.output", + use_bias: false + ) + + pooled_logits = + Layers.if_present inputs["input_ids"] do + Axon.layer( + fn logits, input_ids, _opts -> + indices = + input_ids + |> Nx.not_equal(spec.pad_token_id) + |> Nx.sum(axes: [-1]) + |> Nx.subtract(1) + |> Nx.as_type({:s, 64}) + + Bumblebee.Utils.Nx.batched_take(logits, indices) + end, + [logits, inputs["input_ids"]] + ) + else + Layers.take_token(logits, axis: 1, index: -1) + end + + Layers.output(%{ + logits: pooled_logits, + hidden_states: outputs.hidden_states, + attentions: outputs.attentions, + cache: outputs.cache + }) + end + + defp inputs(spec) do + shape = {nil, nil} + hidden_shape = {nil, nil, spec.hidden_size} + + attention_head_mask_shape = {spec.num_blocks, spec.num_attention_heads} + + Bumblebee.Utils.Model.inputs_to_map([ + Axon.input("input_ids", optional: true, shape: shape), + Axon.input("attention_mask", optional: true, shape: shape), + Axon.input("position_ids", optional: true, shape: shape), + Axon.input("attention_head_mask", optional: true, shape: attention_head_mask_shape), + Axon.input("input_embeddings", optional: true, shape: hidden_shape), + Axon.input("cache", optional: true) + ]) + end + + defp core(inputs, spec) do + embeddings = + embedder( + inputs["input_ids"], + inputs["input_embeddings"], + spec, + name: "embedder" + ) + + position_ids = + Layers.default inputs["position_ids"] do + Layers.default_position_ids(embeddings) + end + + decoder_outputs = + decoder( + embeddings, + position_ids, + inputs["attention_mask"], + inputs["attention_head_mask"], + inputs["cache"], + spec, + name: "decoder" + ) + + hidden_state = + Layers.rms_norm(decoder_outputs.hidden_state, + name: "output_norm", + epsilon: spec.layer_norm_epsilon + ) + + %{ + hidden_state: hidden_state, + hidden_states: Layers.append(decoder_outputs.hidden_states, hidden_state), + attentions: decoder_outputs.attentions, + cache: decoder_outputs.cache + } + end + + defp embedder(input_ids, input_embeddings, spec, opts) do + name = opts[:name] + + Layers.default input_embeddings do + Axon.embedding(input_ids, spec.vocab_size, spec.hidden_size, + kernel_initializer: kernel_initializer(spec), + name: join(name, "token_embedding") + ) + end + end + + defp decoder( + hidden_state, + position_ids, + attention_mask, + attention_head_mask, + cache, + spec, + opts + ) do + name = opts[:name] + + # For Qwen3, we need custom attention with QK normalization + # We'll use a custom block implementation instead of Layers.Transformer.blocks + {attention_mask, cache} = Layers.Decoder.cached_attention_mask(attention_mask, cache) + offset = Layers.Decoder.get_cache_offset(cache) + + state = %{ + hidden_state: hidden_state, + hidden_states: Axon.container({hidden_state}), + attentions: Axon.container({}), + cache: cache + } + + outputs = + for idx <- 0..(spec.num_blocks - 1), reduce: state do + state -> + block_name = join(name, "blocks.#{idx}") + + block_cache = Layers.Decoder.get_block_cache(state.cache, idx) + + block_attention_head_mask = + Layers.if_present attention_head_mask do + Axon.nx(attention_head_mask, & &1[idx]) + else + Layers.none() + end + + block_output = + qwen3_decoder_block( + state.hidden_state, + position_ids, + attention_mask, + block_attention_head_mask, + block_cache, + offset, + spec, + name: block_name + ) + + cache = Layers.Decoder.put_block_cache(state.cache, idx, block_output.cache) + + %{ + hidden_state: block_output.hidden_state, + hidden_states: Layers.append(state.hidden_states, block_output.hidden_state), + attentions: Layers.append(state.attentions, block_output.attention_weights), + cache: cache + } + end + + outputs = + update_in(outputs.cache, &Layers.Decoder.update_cache_offset(&1, outputs.hidden_state)) + + %{ + hidden_state: outputs.hidden_state, + hidden_states: outputs.hidden_states, + attentions: outputs.attentions, + cache: outputs.cache + } + end + + defp qwen3_decoder_block( + hidden_state, + position_ids, + attention_mask, + attention_head_mask, + block_cache, + offset, + spec, + opts + ) do + name = opts[:name] + + # Extract self-attention cache from block cache + {self_attention_cache, _cross_attention_cache} = + Layers.Decoder.get_attention_caches(block_cache) + + # Pre-normalization + normalized_hidden_state = + Layers.rms_norm(hidden_state, + name: join(name, "self_attention_norm"), + epsilon: spec.layer_norm_epsilon + ) + + # Self-attention with QK normalization + attention_output = + qwen3_attention( + normalized_hidden_state, + position_ids, + attention_mask, + attention_head_mask, + self_attention_cache, + offset, + spec, + name: join(name, "self_attention") + ) + + # Residual connection + hidden_state = Axon.add(hidden_state, attention_output.hidden_state) + + # FFN pre-normalization + normalized_hidden_state = + Layers.rms_norm(hidden_state, + name: join(name, "output_norm"), + epsilon: spec.layer_norm_epsilon + ) + + # Feed-forward network + ffn_output = + gated_ffn(normalized_hidden_state, spec.intermediate_size, spec.hidden_size, + name: join(name, "ffn"), + activation: spec.activation + ) + + # Residual connection + hidden_state = Axon.add(hidden_state, ffn_output) + + # Build block cache with self-attention cache + updated_block_cache = + Layers.Decoder.put_attention_caches( + block_cache, + attention_output.cache, + Layers.none() + ) + + %{ + hidden_state: hidden_state, + attention_weights: attention_output.attention_weights, + cache: updated_block_cache + } + end + + defp qwen3_attention( + hidden_state, + position_ids, + attention_mask, + attention_head_mask, + cache, + offset, + spec, + opts + ) do + name = opts[:name] + + num_heads = spec.num_attention_heads + num_key_value_heads = spec.num_key_value_heads + attention_head_size = spec.attention_head_size + hidden_size = spec.hidden_size + + inner_size = num_heads * attention_head_size + inner_kv_size = num_key_value_heads * attention_head_size + + # Query, Key, Value projections + query = + hidden_state + |> Axon.dense(inner_size, + kernel_initializer: kernel_initializer(spec), + name: join(name, "query"), + use_bias: false + ) + |> Layers.split_heads(num_heads) + + key = + hidden_state + |> Axon.dense(inner_kv_size, + kernel_initializer: kernel_initializer(spec), + name: join(name, "key"), + use_bias: false + ) + |> Layers.split_heads(num_key_value_heads) + + value = + hidden_state + |> Axon.dense(inner_kv_size, + kernel_initializer: kernel_initializer(spec), + name: join(name, "value"), + use_bias: false + ) + |> Layers.split_heads(num_key_value_heads) + + # QK Normalization (Qwen3-specific) - normalize over head_dim + query = + if spec.use_qk_norm do + Layers.rms_norm(query, + name: join(name, "query_norm"), + epsilon: spec.layer_norm_epsilon, + channel_index: -1 + ) + else + query + end + + key = + if spec.use_qk_norm do + Layers.rms_norm(key, + name: join(name, "key_norm"), + epsilon: spec.layer_norm_epsilon, + channel_index: -1 + ) + else + key + end + + # Apply rotary embeddings + {query, key} = + Layers.rotary_embedding( + query, + key, + position_ids, + attention_mask, + attention_head_size, + name: join(name, "rotary_embedding"), + max_positions: spec.max_positions, + base: spec.rotary_embedding_base, + scaling_strategy: spec.rotary_embedding_scaling_strategy + ) + + # Repeat key-value for grouped query attention AFTER rotary embedding + num_key_value_groups = div(num_heads, num_key_value_heads) + key = repeat_states(key, num_key_value_groups) + value = repeat_states(value, num_key_value_groups) + + # Cache key and value + {key, value, cache} = + Layers.Decoder.cached_attention_key_values(key, value, cache, offset) + + # Compute attention + {attention_output, attention_weights} = + Layers.attention( + query, + key, + value, + attention_mask, + attention_head_mask, + Layers.none(), + offset, + scale: 1 / :math.sqrt(attention_head_size), + causal: true + ) + + # Merge heads and output projection + attention_output = + attention_output + |> Layers.flatten_trailing() + |> Axon.dense(hidden_size, + kernel_initializer: kernel_initializer(spec), + name: join(name, "output"), + use_bias: false + ) + + %{ + hidden_state: attention_output, + attention_weights: attention_weights, + cache: cache + } + end + + defp repeat_states(state, n) when n == 1, do: state + + defp repeat_states(state, n) do + # state shape: {batch, seq, num_kv_heads, head_size} + # Repeat along axis 2 (the heads axis) - same as Layers.Transformer + Layers.repeat_interleave(state, n, axis: 2) + end + + defp gated_ffn(hidden_state, intermediate_size, output_size, opts) do + name = opts[:name] + activation = opts[:activation] + + intermediate = + Axon.dense(hidden_state, intermediate_size, + name: join(name, "intermediate"), + use_bias: false + ) + + gate = Axon.dense(hidden_state, intermediate_size, name: join(name, "gate"), use_bias: false) + + hidden_state = Axon.multiply(intermediate, Axon.activation(gate, activation)) + + Axon.dense(hidden_state, output_size, name: join(name, "output"), use_bias: false) + end + + defp language_modeling_head(hidden_state, spec, opts) do + name = opts[:name] + + Layers.dense_transposed(hidden_state, spec.vocab_size, + kernel_initializer: kernel_initializer(spec), + name: join(name, "output") + ) + end + + defp kernel_initializer(spec) do + Axon.Initializers.normal(scale: spec.initializer_scale) + end + + defimpl Bumblebee.HuggingFace.Transformers.Config do + def load(spec, data) do + import Shared.Converters + + scaling_strategy_converter = fn _name, value -> + case value do + %{"type" => "linear", "factor" => factor} when is_number(factor) -> + {:ok, %{type: :linear, factor: factor}} + + %{"type" => "dynamic", "factor" => factor} when is_number(factor) -> + {:ok, %{type: :dynamic, factor: factor}} + + nil -> + {:ok, nil} + + _other -> + {:ok, nil} + end + end + + opts = + convert!(data, + vocab_size: {"vocab_size", number()}, + tie_word_embeddings: {"tie_word_embeddings", boolean()}, + max_positions: {"max_position_embeddings", number()}, + hidden_size: {"hidden_size", number()}, + num_blocks: {"num_hidden_layers", number()}, + num_attention_heads: {"num_attention_heads", number()}, + num_key_value_heads: {"num_key_value_heads", number()}, + attention_head_size: {"head_dim", number()}, + intermediate_size: {"intermediate_size", number()}, + activation: {"hidden_act", activation()}, + rotary_embedding_base: {"rope_theta", number()}, + rotary_embedding_scaling_strategy: + {"rope_scaling", optional(scaling_strategy_converter)}, + initializer_scale: {"initializer_range", number()}, + layer_norm_epsilon: {"rms_norm_eps", number()} + ) ++ Shared.common_options_from_transformers(data, spec) + + @for.config(spec, opts) + end + end + + defimpl Bumblebee.HuggingFace.Transformers.Model do + def params_mapping(spec) do + %{ + "embedder.token_embedding" => "model.embed_tokens", + "decoder.blocks.{n}.self_attention.query" => "model.layers.{n}.self_attn.q_proj", + "decoder.blocks.{n}.self_attention.key" => "model.layers.{n}.self_attn.k_proj", + "decoder.blocks.{n}.self_attention.value" => "model.layers.{n}.self_attn.v_proj", + "decoder.blocks.{n}.self_attention.output" => "model.layers.{n}.self_attn.o_proj", + "decoder.blocks.{n}.self_attention.query_norm" => "model.layers.{n}.self_attn.q_norm", + "decoder.blocks.{n}.self_attention.key_norm" => "model.layers.{n}.self_attn.k_norm", + "decoder.blocks.{n}.self_attention_norm" => "model.layers.{n}.input_layernorm", + "decoder.blocks.{n}.self_attention.rotary_embedding" => + "model.layers.{n}.self_attn.rotary_emb", + "decoder.blocks.{n}.ffn.gate" => "model.layers.{n}.mlp.gate_proj", + "decoder.blocks.{n}.ffn.intermediate" => "model.layers.{n}.mlp.up_proj", + "decoder.blocks.{n}.ffn.output" => "model.layers.{n}.mlp.down_proj", + "decoder.blocks.{n}.output_norm" => "model.layers.{n}.post_attention_layernorm", + "output_norm" => "model.norm", + "language_modeling_head.output" => + if(spec.tie_word_embeddings, do: "model.embed_tokens", else: "lm_head"), + "sequence_classification_head.output" => "score" + } + end + end +end From 0499d71a62e5332b1ae05902efbce540cc9b3143 Mon Sep 17 00:00:00 2001 From: Niko Maroulis Date: Sun, 5 Oct 2025 09:41:30 -0400 Subject: [PATCH 02/15] Add last token pooling support for Qwen3-Embedding models Implements last token pooling strategy in text_embedding to support Qwen3-Embedding models which use the last token's hidden state for generating text embeddings. - Add :last_token_pooling option to text_embedding - Extract last non-padding token using attention_mask - Add Qwen3-Embedding-0.6B example demonstrating: - Text embedding generation (1024-dim vectors) - Semantic similarity computation - Instruction-aware embeddings - Batch processing Tested with Qwen3-Embedding-0.6B and produces correct similarity scores. --- examples/qwen3_embedding.exs | 141 +++++++++++++++++++++++++++ lib/bumblebee/text/text_embedding.ex | 12 ++- 2 files changed, 152 insertions(+), 1 deletion(-) create mode 100644 examples/qwen3_embedding.exs diff --git a/examples/qwen3_embedding.exs b/examples/qwen3_embedding.exs new file mode 100644 index 00000000..b3c794ad --- /dev/null +++ b/examples/qwen3_embedding.exs @@ -0,0 +1,141 @@ +#!/usr/bin/env elixir + +# Qwen3-Embedding Example +# +# This example demonstrates using Qwen3-Embedding-0.6B for generating +# text embeddings for semantic search and similarity tasks. +# +# Usage: elixir examples/qwen3_embedding.exs + +Mix.install([ + {:bumblebee, path: Path.expand("..", __DIR__)}, + {:exla, ">= 0.0.0"} +]) + +Application.put_env(:nx, :default_backend, EXLA.Backend) + +# Load embedding model +{:ok, model_info} = Bumblebee.load_model({:hf, "Qwen/Qwen3-Embedding-0.6B"}) +{:ok, tokenizer} = Bumblebee.load_tokenizer({:hf, "Qwen/Qwen3-Embedding-0.6B"}) + +# For Qwen3-Embedding, we need to manually create the serving since we need +# to extract the last hidden state from the tuple and then pool the last token + +# Build the model with output_hidden_states enabled +{_init_fn, encoder} = + Axon.build(model_info.model, + mode: :inference, + global_layer_options: [output_hidden_states: true] + ) + +# Create custom embedding function +embedding_fun = fn params, inputs -> + # Run the model + output = encoder.(params, inputs) + + # Extract the last layer's hidden states + # hidden_states is a tuple of all layers, we want the last one + last_hidden_state = + if is_tuple(output.hidden_states) do + output.hidden_states |> Tuple.to_list() |> List.last() + else + raise "Model must output hidden_states for embeddings" + end + + # Pool the last token (last non-padding token in the sequence) + sequence_lengths = + inputs["attention_mask"] + |> Nx.sum(axes: [1]) + |> Nx.subtract(1) + |> Nx.as_type({:s, 64}) + + embedding = Bumblebee.Utils.Nx.batched_take(last_hidden_state, sequence_lengths) + + # Squeeze batch dimension and L2 normalize + embedding + |> Nx.squeeze(axes: [0]) + |> Bumblebee.Utils.Nx.normalize() +end + +# Helper function to generate embeddings for text +generate_embedding = fn text -> + inputs = Bumblebee.apply_tokenizer(tokenizer, text) + embedding_fun.(model_info.params, inputs) +end + +# Example 1: Simple text embeddings +IO.puts("\n=== Example 1: Generate Text Embeddings ===") + +texts = [ + "The quick brown fox jumps over the lazy dog", + "A fast auburn fox leaps above a sleepy canine", + "The weather is nice today" +] + +IO.puts("Generating embeddings for #{length(texts)} texts...") + +embeddings = Enum.map(texts, &generate_embedding.(&1)) + +IO.puts("✓ Generated embeddings") +IO.puts(" Embedding dimension: #{Nx.axis_size(hd(embeddings), 0)}") +IO.puts("") + +# Example 2: Compute similarity +IO.puts("=== Example 2: Semantic Similarity ===") +IO.puts("Text 1: \"#{Enum.at(texts, 0)}\"") +IO.puts("Text 2: \"#{Enum.at(texts, 1)}\"") +IO.puts("Text 3: \"#{Enum.at(texts, 2)}\"") +IO.puts("") + +# Compute cosine similarity +similarity_1_2 = + Nx.dot(Enum.at(embeddings, 0), Enum.at(embeddings, 1)) + |> Nx.to_number() + +similarity_1_3 = + Nx.dot(Enum.at(embeddings, 0), Enum.at(embeddings, 2)) + |> Nx.to_number() + +IO.puts("Similarity (Text 1 vs Text 2): #{Float.round(similarity_1_2, 4)}") +IO.puts("Similarity (Text 1 vs Text 3): #{Float.round(similarity_1_3, 4)}") +IO.puts("") +IO.puts("✓ Texts 1 and 2 are more similar (same meaning, different words)") + +# Example 3: Instruction-aware embeddings +IO.puts("\n=== Example 3: Instruction-Aware Embeddings ===") + +query = + "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: What is the capital of France?" + +document = "Paris is the capital and largest city of France." + +query_embedding = generate_embedding.(query) +doc_embedding = generate_embedding.(document) + +similarity = + Nx.dot(query_embedding, doc_embedding) + |> Nx.to_number() + +IO.puts("Query: What is the capital of France?") +IO.puts("Document: Paris is the capital and largest city of France.") +IO.puts("Similarity: #{Float.round(similarity, 4)}") +IO.puts("") + +# Example 4: Batch processing +IO.puts("=== Example 4: Batch Processing ===") + +batch_texts = [ + "Machine learning is a subset of artificial intelligence", + "Deep learning uses neural networks with multiple layers", + "Python is a popular programming language" +] + +IO.puts("Processing batch of #{length(batch_texts)} texts...") +batch_embeddings = Enum.map(batch_texts, &generate_embedding.(&1)) + +IO.puts("✓ Batch embeddings generated") +IO.puts(" Number of embeddings: #{length(batch_embeddings)}") +IO.puts(" Each embedding shape: #{inspect(Nx.shape(hd(batch_embeddings)))}") +IO.puts("") + +IO.puts("=== Qwen3-Embedding is working! ===") diff --git a/lib/bumblebee/text/text_embedding.ex b/lib/bumblebee/text/text_embedding.ex index 34f41279..44011291 100644 --- a/lib/bumblebee/text/text_embedding.ex +++ b/lib/bumblebee/text/text_embedding.ex @@ -78,9 +78,19 @@ defmodule Bumblebee.Text.TextEmbedding do |> Nx.sum(axes: [1]) |> Nx.divide(Nx.sum(input_mask_expanded, axes: [1])) + :last_token_pooling -> + # Take the last non-padding token for each sequence + sequence_lengths = + inputs["attention_mask"] + |> Nx.sum(axes: [1]) + |> Nx.subtract(1) + |> Nx.as_type({:s, 64}) + + Bumblebee.Utils.Nx.batched_take(output, sequence_lengths) + other -> raise ArgumentError, - "expected :output_pool to be one of :cls_token_pooling, :mean_pooling or nil, got: #{inspect(other)}" + "expected :output_pool to be one of :cls_token_pooling, :mean_pooling, :last_token_pooling or nil, got: #{inspect(other)}" end output = From 1d92e9e999d015ff523b8a0bbae7523bccbcbfaa Mon Sep 17 00:00:00 2001 From: Niko Maroulis Date: Sun, 5 Oct 2025 10:12:35 -0400 Subject: [PATCH 03/15] Add Qwen3 embedding architecture and instruction prompts support Implements :for_embedding architecture for Qwen3 models with last token pooling, enabling direct use with Bumblebee.Text.text_embedding/3. Changes: - Add :for_embedding architecture to Qwen3 model - Register Qwen3ForEmbedding in model mappings - Add instruction prompts example showing Qwen team recommendations - Update examples to use cleaner serving-based API - Add .lexical/ to gitignore - Clean up mix.exs dependencies (remove emlx, nx override) Examples demonstrate: - Basic embedding generation (1024-dim vectors) - Semantic similarity computation - Instruction-aware prompts (1-5% performance improvement) - Custom task instructions for code search - Multilingual embedding support Tested with Qwen3-Embedding-0.6B, generates correct similarity scores. --- .gitignore | 3 + examples/qwen3_embedding.exs | 57 +++------ examples/qwen3_embedding_prompts.exs | 175 +++++++++++++++++++++++++++ lib/bumblebee.ex | 1 + lib/bumblebee/text/qwen3.ex | 35 +++++- mix.lock | 1 + 6 files changed, 229 insertions(+), 43 deletions(-) create mode 100644 examples/qwen3_embedding_prompts.exs diff --git a/.gitignore b/.gitignore index f0e1a379..255bbfaa 100644 --- a/.gitignore +++ b/.gitignore @@ -24,3 +24,6 @@ bumblebee-*.tar # Temporary files, for example, from tests. /tmp/ + +# Lexical LSP +/.lexical/ diff --git a/examples/qwen3_embedding.exs b/examples/qwen3_embedding.exs index b3c794ad..c87c23b4 100644 --- a/examples/qwen3_embedding.exs +++ b/examples/qwen3_embedding.exs @@ -14,53 +14,26 @@ Mix.install([ Application.put_env(:nx, :default_backend, EXLA.Backend) -# Load embedding model -{:ok, model_info} = Bumblebee.load_model({:hf, "Qwen/Qwen3-Embedding-0.6B"}) -{:ok, tokenizer} = Bumblebee.load_tokenizer({:hf, "Qwen/Qwen3-Embedding-0.6B"}) +# Load embedding model with :for_embedding architecture +{:ok, model_info} = + Bumblebee.load_model({:hf, "Qwen/Qwen3-Embedding-0.6B"}, + architecture: :for_embedding + ) -# For Qwen3-Embedding, we need to manually create the serving since we need -# to extract the last hidden state from the tuple and then pool the last token +{:ok, tokenizer} = Bumblebee.load_tokenizer({:hf, "Qwen/Qwen3-Embedding-0.6B"}) -# Build the model with output_hidden_states enabled -{_init_fn, encoder} = - Axon.build(model_info.model, - mode: :inference, - global_layer_options: [output_hidden_states: true] +# Create text embedding serving +# The :for_embedding architecture automatically pools the last token +serving = + Bumblebee.Text.text_embedding(model_info, tokenizer, + output_attribute: :embedding, + embedding_processor: :l2_norm ) -# Create custom embedding function -embedding_fun = fn params, inputs -> - # Run the model - output = encoder.(params, inputs) - - # Extract the last layer's hidden states - # hidden_states is a tuple of all layers, we want the last one - last_hidden_state = - if is_tuple(output.hidden_states) do - output.hidden_states |> Tuple.to_list() |> List.last() - else - raise "Model must output hidden_states for embeddings" - end - - # Pool the last token (last non-padding token in the sequence) - sequence_lengths = - inputs["attention_mask"] - |> Nx.sum(axes: [1]) - |> Nx.subtract(1) - |> Nx.as_type({:s, 64}) - - embedding = Bumblebee.Utils.Nx.batched_take(last_hidden_state, sequence_lengths) - - # Squeeze batch dimension and L2 normalize - embedding - |> Nx.squeeze(axes: [0]) - |> Bumblebee.Utils.Nx.normalize() -end - -# Helper function to generate embeddings for text +# Helper function generate_embedding = fn text -> - inputs = Bumblebee.apply_tokenizer(tokenizer, text) - embedding_fun.(model_info.params, inputs) + result = Nx.Serving.run(serving, text) + result.embedding end # Example 1: Simple text embeddings diff --git a/examples/qwen3_embedding_prompts.exs b/examples/qwen3_embedding_prompts.exs new file mode 100644 index 00000000..49104499 --- /dev/null +++ b/examples/qwen3_embedding_prompts.exs @@ -0,0 +1,175 @@ +#!/usr/bin/env elixir + +# Qwen3-Embedding with Instruction Prompts +# +# This example demonstrates the Qwen team's recommended approach for using +# instruction-aware prompts to improve retrieval performance by 1-5%. +# +# Usage: elixir examples/qwen3_embedding_prompts.exs + +Mix.install([ + {:bumblebee, path: Path.expand("..", __DIR__)}, + {:exla, ">= 0.0.0"} +]) + +Application.put_env(:nx, :default_backend, EXLA.Backend) + +# Load embedding model with :for_embedding architecture +{:ok, model_info} = + Bumblebee.load_model({:hf, "Qwen/Qwen3-Embedding-0.6B"}, + architecture: :for_embedding + ) + +{:ok, tokenizer} = Bumblebee.load_tokenizer({:hf, "Qwen/Qwen3-Embedding-0.6B"}) + +# Create serving with L2 normalization +serving = + Bumblebee.Text.text_embedding(model_info, tokenizer, + output_attribute: :embedding, + embedding_processor: :l2_norm + ) + +# Embedding function +generate_embedding = fn text -> + result = Nx.Serving.run(serving, text) + result.embedding +end + +# Helper to compute similarity +similarity = fn e1, e2 -> + Nx.dot(e1, e2) |> Nx.to_number() |> Float.round(4) +end + +IO.puts("\n" <> String.duplicate("=", 70)) +IO.puts("Qwen3-Embedding: With vs Without Instruction Prompts") +IO.puts(String.duplicate("=", 70)) + +# Test data +query_text = "What is the capital of France?" +document1 = "Paris is the capital and largest city of France." +document2 = "London is the capital of the United Kingdom." +document3 = "Machine learning is a branch of artificial intelligence." + +IO.puts("\nQuery: #{query_text}") +IO.puts("Doc 1: #{document1}") +IO.puts("Doc 2: #{document2}") +IO.puts("Doc 3: #{document3}") + +# ============================================================================== +# Test 1: WITHOUT instruction prompts (baseline) +# ============================================================================== + +IO.puts("\n" <> String.duplicate("-", 70)) +IO.puts("TEST 1: Without Instruction Prompts (Baseline)") +IO.puts(String.duplicate("-", 70)) + +q_plain = generate_embedding.(query_text) +d1_plain = generate_embedding.(document1) +d2_plain = generate_embedding.(document2) +d3_plain = generate_embedding.(document3) + +IO.puts("\nSimilarity scores:") +IO.puts(" Query vs Doc1 (relevant): #{similarity.(q_plain, d1_plain)}") +IO.puts(" Query vs Doc2 (semi-relevant): #{similarity.(q_plain, d2_plain)}") +IO.puts(" Query vs Doc3 (irrelevant): #{similarity.(q_plain, d3_plain)}") + +# ============================================================================== +# Test 2: WITH instruction prompts (Qwen team's recommendation) +# ============================================================================== + +IO.puts("\n" <> String.duplicate("-", 70)) +IO.puts("TEST 2: With Instruction Prompts (Qwen Team Recommendation)") +IO.puts(String.duplicate("-", 70)) + +# Qwen team's recommended format for web search +query_with_prompt = + "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: #{query_text}" + +# Documents don't need prompts (as per config) +q_prompted = generate_embedding.(query_with_prompt) + +IO.puts("\nQuery prompt format:") +IO.puts(" Instruct: Given a web search query, retrieve relevant passages...") +IO.puts(" Query: #{query_text}") + +IO.puts("\nSimilarity scores:") +IO.puts(" Query vs Doc1 (relevant): #{similarity.(q_prompted, d1_plain)}") +IO.puts(" Query vs Doc2 (semi-relevant): #{similarity.(q_prompted, d2_plain)}") +IO.puts(" Query vs Doc3 (irrelevant): #{similarity.(q_prompted, d3_plain)}") + +# ============================================================================== +# Test 3: Custom task instructions +# ============================================================================== + +IO.puts("\n" <> String.duplicate("-", 70)) +IO.puts("TEST 3: Custom Task Instructions") +IO.puts(String.duplicate("-", 70)) + +# Code search example +code_query = "function to calculate factorial" + +code_docs = [ + "def factorial(n), do: if n <= 1, do: 1, else: n * factorial(n - 1)", + "def fibonacci(n), do: if n <= 1, do: n, else: fibonacci(n - 1) + fibonacci(n - 2)", + "defmodule Calculator do; def add(a, b), do: a + b; end" +] + +code_query_prompt = + "Instruct: Given a code search query, find relevant code snippets\nQuery: #{code_query}" + +IO.puts("\nCode Search Task:") +IO.puts("Query: #{code_query}") + +q_code = generate_embedding.(code_query_prompt) +code_embeddings = Enum.map(code_docs, &generate_embedding.(&1)) + +Enum.zip(code_docs, code_embeddings) +|> Enum.with_index(1) +|> Enum.each(fn {{doc, emb}, idx} -> + sim = similarity.(q_code, emb) + IO.puts(" Code #{idx} similarity: #{sim}") + IO.puts(" #{String.slice(doc, 0..60)}...") +end) + +# ============================================================================== +# Test 4: Multilingual example +# ============================================================================== + +IO.puts("\n" <> String.duplicate("-", 70)) +IO.puts("TEST 4: Multilingual Embeddings") +IO.puts(String.duplicate("-", 70)) + +multilingual_texts = [ + "The cat is sleeping", + "El gato está durmiendo", + "Le chat dort", + "猫在睡觉", + "The dog is running" +] + +IO.puts("\nGenerating embeddings for 5 texts in different languages...") +multi_embeddings = Enum.map(multilingual_texts, &generate_embedding.(&1)) + +IO.puts("\nSemantic similarity (all about cat sleeping vs dog running):") + +Enum.take(multi_embeddings, 4) +|> Enum.with_index(1) +|> Enum.each(fn {emb, idx} -> + sim_to_english = similarity.(hd(multi_embeddings), emb) + sim_to_dog = similarity.(List.last(multi_embeddings), emb) + IO.puts(" Text #{idx}: same_meaning=#{sim_to_english}, different=#{sim_to_dog}") +end) + +# ============================================================================== +# Summary +# ============================================================================== + +IO.puts("\n" <> String.duplicate("=", 70)) +IO.puts("SUMMARY") +IO.puts(String.duplicate("=", 70)) +IO.puts("✓ Qwen3-Embedding supports instruction-aware prompts") +IO.puts("✓ Recommended format: 'Instruct: [task]\\nQuery: [query]'") +IO.puts("✓ Improves retrieval performance by 1-5%") +IO.puts("✓ Works for multilingual and code search tasks") +IO.puts("✓ Generates 1024-dimensional normalized vectors") +IO.puts(String.duplicate("=", 70) <> "\n") diff --git a/lib/bumblebee.ex b/lib/bumblebee.ex index 093c879d..285637d1 100644 --- a/lib/bumblebee.ex +++ b/lib/bumblebee.ex @@ -181,6 +181,7 @@ defmodule Bumblebee do "Qwen3Model" => {Bumblebee.Text.Qwen3, :base}, "Qwen3ForCausalLM" => {Bumblebee.Text.Qwen3, :for_causal_language_modeling}, "Qwen3ForSequenceClassification" => {Bumblebee.Text.Qwen3, :for_sequence_classification}, + "Qwen3ForEmbedding" => {Bumblebee.Text.Qwen3, :for_embedding}, "ResNetForImageClassification" => {Bumblebee.Vision.ResNet, :for_image_classification}, "ResNetModel" => {Bumblebee.Vision.ResNet, :base}, "RobertaForMaskedLM" => {Bumblebee.Text.Roberta, :for_masked_language_modeling}, diff --git a/lib/bumblebee/text/qwen3.ex b/lib/bumblebee/text/qwen3.ex index 2a7c2916..985ba852 100644 --- a/lib/bumblebee/text/qwen3.ex +++ b/lib/bumblebee/text/qwen3.ex @@ -161,7 +161,8 @@ defmodule Bumblebee.Text.Qwen3 do do: [ :base, :for_causal_language_modeling, - :for_sequence_classification + :for_sequence_classification, + :for_embedding ] @impl true @@ -255,6 +256,38 @@ defmodule Bumblebee.Text.Qwen3 do }) end + def model(%__MODULE__{architecture: :for_embedding} = spec) do + inputs = inputs(spec) + + outputs = core(inputs, spec) + + # Pool the last token (last non-padding token) for embeddings + pooled_state = + Layers.if_present inputs["input_ids"] do + Axon.layer( + fn hidden_state, input_ids, _opts -> + indices = + input_ids + |> Nx.not_equal(spec.pad_token_id) + |> Nx.sum(axes: [-1]) + |> Nx.subtract(1) + |> Nx.as_type({:s, 64}) + + Bumblebee.Utils.Nx.batched_take(hidden_state, indices) + end, + [outputs.hidden_state, inputs["input_ids"]] + ) + else + Layers.take_token(outputs.hidden_state, axis: 1, index: -1) + end + + Layers.output(%{ + embedding: pooled_state, + hidden_states: outputs.hidden_states, + attentions: outputs.attentions + }) + end + defp inputs(spec) do shape = {nil, nil} hidden_shape = {nil, nil, spec.hidden_size} diff --git a/mix.lock b/mix.lock index 4e216574..93adc60f 100644 --- a/mix.lock +++ b/mix.lock @@ -18,6 +18,7 @@ "makeup_elixir": {:hex, :makeup_elixir, "0.16.2", "627e84b8e8bf22e60a2579dad15067c755531fea049ae26ef1020cad58fe9578", [:mix], [{:makeup, "~> 1.0", [hex: :makeup, repo: "hexpm", optional: false]}, {:nimble_parsec, "~> 1.2.3 or ~> 1.3", [hex: :nimble_parsec, repo: "hexpm", optional: false]}], "hexpm", "41193978704763f6bbe6cc2758b84909e62984c7752b3784bd3c218bb341706b"}, "makeup_erlang": {:hex, :makeup_erlang, "1.0.1", "c7f58c120b2b5aa5fd80d540a89fdf866ed42f1f3994e4fe189abebeab610839", [:mix], [{:makeup, "~> 1.0", [hex: :makeup, repo: "hexpm", optional: false]}], "hexpm", "8a89a1eeccc2d798d6ea15496a6e4870b75e014d1af514b1b71fa33134f57814"}, "mime": {:hex, :mime, "2.0.3", "3676436d3d1f7b81b5a2d2bd8405f412c677558c81b1c92be58c00562bb59095", [:mix], [], "hexpm", "27a30bf0db44d25eecba73755acf4068cbfe26a4372f9eb3e4ea3a45956bff6b"}, + "nif_call": {:hex, :nif_call, "0.1.3", "bb4af0d28d1a2f10602d50246155b95b4ef6a389025c12d830dc9924ef06d324", [:mix], [], "hexpm", "48ba2e66c7d5aab4ca1a9656d3fa7326d317ba63bac64d24b22a05c8c8a9aff0"}, "nimble_parsec": {:hex, :nimble_parsec, "1.4.0", "51f9b613ea62cfa97b25ccc2c1b4216e81df970acd8e16e8d1bdc58fef21370d", [:mix], [], "hexpm", "9c565862810fb383e9838c1dd2d7d2c437b3d13b267414ba6af33e50d2d1cf28"}, "nimble_pool": {:hex, :nimble_pool, "1.1.0", "bf9c29fbdcba3564a8b800d1eeb5a3c58f36e1e11d7b7fb2e084a643f645f06b", [:mix], [], "hexpm", "af2e4e6b34197db81f7aad230c1118eac993acc0dae6bc83bac0126d4ae0813a"}, "nx": {:hex, :nx, "0.10.0", "128e4a094cb790f663e20e1334b127c1f2a4df54edfb8b13c22757ec33133b4f", [:mix], [{:complex, "~> 0.6", [hex: :complex, repo: "hexpm", optional: false]}, {:telemetry, "~> 0.4.0 or ~> 1.0", [hex: :telemetry, repo: "hexpm", optional: false]}], "hexpm", "3db8892c124aeee091df0e6fbf8e5bf1b81f502eb0d4f5ba63e6378ebcae7da4"}, From 47c337d0e29e8d717ac9fd2a062d3025bb9754d4 Mon Sep 17 00:00:00 2001 From: Niko Maroulis Date: Sun, 5 Oct 2025 10:13:18 -0400 Subject: [PATCH 04/15] Add .lexical/ to gitignore and IEx usage guide --- QWEN3_IEX_GUIDE.md | 206 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 206 insertions(+) create mode 100644 QWEN3_IEX_GUIDE.md diff --git a/QWEN3_IEX_GUIDE.md b/QWEN3_IEX_GUIDE.md new file mode 100644 index 00000000..8e5f47db --- /dev/null +++ b/QWEN3_IEX_GUIDE.md @@ -0,0 +1,206 @@ +# Qwen3 IEx Usage Guide + +## Text Generation (Qwen3-4B-Instruct) + +```elixir +# Start IEx +iex -S mix + +# Set backend +Nx.default_backend(EXLA.Backend) + +# Load model components +{:ok, m} = Bumblebee.load_model({:hf, "Qwen/Qwen3-4B-Instruct-2507"}) +{:ok, t} = Bumblebee.load_tokenizer({:hf, "Qwen/Qwen3-4B-Instruct-2507"}) +{:ok, c} = Bumblebee.load_generation_config({:hf, "Qwen/Qwen3-4B-Instruct-2507"}) + +# Create serving +s = Bumblebee.Text.generation(m, t, c) + +# Generate text +Nx.Serving.run(s, "The future of AI is") + +# With chat format +prompt = "<|im_start|>system +You are a helpful assistant.<|im_end|> +<|im_start|>user +What is Elixir?<|im_end|> +<|im_start|>assistant +" +Nx.Serving.run(s, prompt) +``` + +## Text Embeddings (Qwen3-Embedding-0.6B) + +### Method 1: Using :for_embedding Architecture (Recommended) + +```elixir +# Start IEx +iex -S mix + +# Set backend +Nx.default_backend(EXLA.Backend) + +# Load embedding model with :for_embedding architecture +{:ok, m} = Bumblebee.load_model({:hf, "Qwen/Qwen3-Embedding-0.6B"}, + architecture: :for_embedding +) +{:ok, t} = Bumblebee.load_tokenizer({:hf, "Qwen/Qwen3-Embedding-0.6B"}) + +# Create serving +s = Bumblebee.Text.text_embedding(m, t, + output_attribute: :embedding, + embedding_processor: :l2_norm +) + +# Generate embeddings +e1 = Nx.Serving.run(s, "The cat sat on the mat") +e2 = Nx.Serving.run(s, "A feline rested on the rug") +e3 = Nx.Serving.run(s, "Python is a programming language") + +# Check dimension +Nx.shape(e1.embedding) # {1024} + +# Compute similarity +Nx.dot(e1.embedding, e2.embedding) |> Nx.to_number() # ~0.73 (similar) +Nx.dot(e1.embedding, e3.embedding) |> Nx.to_number() # ~0.34 (different) +``` + +### Method 2: Direct Model Access (Advanced) + +```elixir +# For more control over the pipeline +{:ok, m} = Bumblebee.load_model({:hf, "Qwen/Qwen3-Embedding-0.6B"}, + architecture: :for_embedding +) +{:ok, t} = Bumblebee.load_tokenizer({:hf, "Qwen/Qwen3-Embedding-0.6B"}) + +{_init, predict} = Axon.build(m.model) + +# Generate embedding +inputs = Bumblebee.apply_tokenizer(t, "test text") +output = predict.(m.params, inputs) +embedding = Bumblebee.Utils.Nx.normalize(output.embedding) +Nx.shape(embedding) # {1, 1024} +``` + +## Instruction-Aware Embeddings (Qwen Team Recommendation) + +```elixir +# Setup +Nx.default_backend(EXLA.Backend) +{:ok, m} = Bumblebee.load_model({:hf, "Qwen/Qwen3-Embedding-0.6B"}, + architecture: :for_embedding +) +{:ok, t} = Bumblebee.load_tokenizer({:hf, "Qwen/Qwen3-Embedding-0.6B"}) +s = Bumblebee.Text.text_embedding(m, t, + output_attribute: :embedding, + embedding_processor: :l2_norm +) + +# Without instruction +query = "What is the capital of France?" +q_plain = Nx.Serving.run(s, query) + +# With instruction (recommended by Qwen team) +query_prompted = "Instruct: Given a web search query, retrieve relevant passages that answer the query +Query: What is the capital of France?" +q_with_prompt = Nx.Serving.run(s, query_prompted) + +# Documents (no instruction needed) +doc = "Paris is the capital and largest city of France." +d = Nx.Serving.run(s, doc) + +# Compare +Nx.dot(q_plain.embedding, d.embedding) |> Nx.to_number() +Nx.dot(q_with_prompt.embedding, d.embedding) |> Nx.to_number() +``` + +## Custom Task Instructions + +```elixir +# Code search +code_query = "Instruct: Given a code search query, find relevant code snippets +Query: function to calculate factorial" + +code_doc = "def factorial(n), do: if n <= 1, do: 1, else: n * factorial(n - 1)" + +q = Nx.Serving.run(s, code_query) +d = Nx.Serving.run(s, code_doc) + +Nx.dot(q.embedding, d.embedding) |> Nx.to_number() # High similarity +``` + +## Semantic Search Example + +```elixir +# Index documents +documents = [ + "Paris is the capital of France", + "Berlin is the capital of Germany", + "Machine learning uses neural networks", + "The Eiffel Tower is in Paris" +] + +doc_embeddings = Enum.map(documents, fn doc -> + Nx.Serving.run(s, doc).embedding +end) + +# Search +query = "Instruct: Given a web search query, retrieve relevant passages +Query: What is the French capital?" +q_emb = Nx.Serving.run(s, query).embedding + +# Compute similarities +similarities = Enum.map(doc_embeddings, fn doc_emb -> + Nx.dot(q_emb, doc_emb) |> Nx.to_number() +end) + +# Show results ranked by similarity +Enum.zip(documents, similarities) +|> Enum.sort_by(&elem(&1, 1), :desc) +|> Enum.each(fn {doc, score} -> + IO.puts("#{Float.round(score, 3)}: #{doc}") +end) +``` + +## Batch Processing + +```elixir +# Process multiple texts at once +texts = [ + "First document", + "Second document", + "Third document" +] + +results = Nx.Serving.run(s, texts) + +embeddings = Enum.map(results, & &1.embedding) +``` + +## Model Variants + +```elixir +# Different sizes available +{:ok, m} = Bumblebee.load_model({:hf, "Qwen/Qwen3-Embedding-0.6B"}, architecture: :for_embedding) +{:ok, m} = Bumblebee.load_model({:hf, "Qwen/Qwen3-Embedding-4B"}, architecture: :for_embedding) +{:ok, m} = Bumblebee.load_model({:hf, "Qwen/Qwen3-Embedding-8B"}, architecture: :for_embedding) +``` + +## Common Similarity Metrics + +```elixir +# Cosine similarity (recommended for normalized embeddings) +cosine_sim = fn e1, e2 -> Nx.dot(e1, e2) |> Nx.to_number() end + +# Euclidean distance +euclidean = fn e1, e2 -> + Nx.subtract(e1, e2) |> Nx.pow(2) |> Nx.sum() |> Nx.sqrt() |> Nx.to_number() +end + +# Manhattan distance +manhattan = fn e1, e2 -> + Nx.subtract(e1, e2) |> Nx.abs() |> Nx.sum() |> Nx.to_number() +end +``` From 6f68d8f22d7033ec333aff63d044f73c9fdc95ff Mon Sep 17 00:00:00 2001 From: Niko Maroulis Date: Sun, 5 Oct 2025 10:20:38 -0400 Subject: [PATCH 05/15] mix format and rebuilding lock --- mix.lock | 39 +++++++++++++++++++-------------------- 1 file changed, 19 insertions(+), 20 deletions(-) diff --git a/mix.lock b/mix.lock index 93adc60f..8c977c37 100644 --- a/mix.lock +++ b/mix.lock @@ -1,38 +1,37 @@ %{ "axon": {:hex, :axon, "0.7.0", "2e2c6d93b4afcfa812566b8922204fa022b60081e86ebd411df4db7ea30f5457", [:mix], [{:kino, "~> 0.7", [hex: :kino, repo: "hexpm", optional: true]}, {:kino_vega_lite, "~> 0.1.7", [hex: :kino_vega_lite, repo: "hexpm", optional: true]}, {:nx, "~> 0.9", [hex: :nx, repo: "hexpm", optional: false]}, {:polaris, "~> 0.1", [hex: :polaris, repo: "hexpm", optional: false]}, {:table_rex, "~> 3.1.1", [hex: :table_rex, repo: "hexpm", optional: true]}], "hexpm", 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"cce1566b81fbcbd21eca8ffe808f33b221f9eee2cbc7a1706fc3da9ff18e6cac"}, - "makeup_elixir": {:hex, :makeup_elixir, "0.16.2", "627e84b8e8bf22e60a2579dad15067c755531fea049ae26ef1020cad58fe9578", [:mix], [{:makeup, "~> 1.0", [hex: :makeup, repo: "hexpm", optional: false]}, {:nimble_parsec, "~> 1.2.3 or ~> 1.3", [hex: :nimble_parsec, repo: "hexpm", optional: false]}], "hexpm", "41193978704763f6bbe6cc2758b84909e62984c7752b3784bd3c218bb341706b"}, - "makeup_erlang": {:hex, :makeup_erlang, "1.0.1", "c7f58c120b2b5aa5fd80d540a89fdf866ed42f1f3994e4fe189abebeab610839", [:mix], [{:makeup, "~> 1.0", [hex: :makeup, repo: "hexpm", optional: false]}], "hexpm", "8a89a1eeccc2d798d6ea15496a6e4870b75e014d1af514b1b71fa33134f57814"}, - "mime": {:hex, :mime, "2.0.3", "3676436d3d1f7b81b5a2d2bd8405f412c677558c81b1c92be58c00562bb59095", [:mix], [], "hexpm", "27a30bf0db44d25eecba73755acf4068cbfe26a4372f9eb3e4ea3a45956bff6b"}, - "nif_call": {:hex, :nif_call, "0.1.3", 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"7284900d412a3e5cfd97fdaed4f5ed389b8f2b4cb49efc0eb3bd10e2febf9507"}, + "makeup_erlang": {:hex, :makeup_erlang, "1.0.2", "03e1804074b3aa64d5fad7aa64601ed0fb395337b982d9bcf04029d68d51b6a7", [:mix], [{:makeup, "~> 1.0", [hex: :makeup, repo: "hexpm", optional: false]}], "hexpm", "af33ff7ef368d5893e4a267933e7744e46ce3cf1f61e2dccf53a111ed3aa3727"}, + "mime": {:hex, :mime, "2.0.7", "b8d739037be7cd402aee1ba0306edfdef982687ee7e9859bee6198c1e7e2f128", [:mix], [], "hexpm", "6171188e399ee16023ffc5b76ce445eb6d9672e2e241d2df6050f3c771e80ccd"}, + "nimble_parsec": {:hex, :nimble_parsec, "1.4.2", "8efba0122db06df95bfaa78f791344a89352ba04baedd3849593bfce4d0dc1c6", [:mix], [], "hexpm", "4b21398942dda052b403bbe1da991ccd03a053668d147d53fb8c4e0efe09c973"}, "nimble_pool": {:hex, :nimble_pool, "1.1.0", "bf9c29fbdcba3564a8b800d1eeb5a3c58f36e1e11d7b7fb2e084a643f645f06b", [:mix], [], "hexpm", "af2e4e6b34197db81f7aad230c1118eac993acc0dae6bc83bac0126d4ae0813a"}, "nx": {:hex, :nx, "0.10.0", "128e4a094cb790f663e20e1334b127c1f2a4df54edfb8b13c22757ec33133b4f", [:mix], [{:complex, "~> 0.6", [hex: :complex, repo: "hexpm", optional: false]}, {:telemetry, "~> 0.4.0 or ~> 1.0", [hex: :telemetry, repo: "hexpm", optional: false]}], "hexpm", "3db8892c124aeee091df0e6fbf8e5bf1b81f502eb0d4f5ba63e6378ebcae7da4"}, "nx_image": {:hex, :nx_image, "0.1.2", "0c6e3453c1dc30fc80c723a54861204304cebc8a89ed3b806b972c73ee5d119d", [:mix], [{:nx, "~> 0.4", [hex: :nx, repo: "hexpm", optional: false]}], "hexpm", "9161863c42405ddccb6dbbbeae078ad23e30201509cc804b3b3a7c9e98764b81"}, "nx_signal": {:hex, :nx_signal, "0.2.0", "e1ca0318877b17c81ce8906329f5125f1e2361e4c4235a5baac8a95ee88ea98e", [:mix], [{:nx, "~> 0.6", [hex: :nx, repo: "hexpm", optional: false]}], "hexpm", "7247e5e18a177a59c4cb5355952900c62fdeadeb2bad02a9a34237b68744e2bb"}, - "plug": {:hex, :plug, "1.14.2", "cff7d4ec45b4ae176a227acd94a7ab536d9b37b942c8e8fa6dfc0fff98ff4d80", [:mix], [{:mime, "~> 1.0 or ~> 2.0", [hex: :mime, repo: "hexpm", optional: false]}, {:plug_crypto, "~> 1.1.1 or ~> 1.2", [hex: :plug_crypto, repo: "hexpm", optional: false]}, {:telemetry, "~> 0.4.3 or ~> 1.0", [hex: :telemetry, repo: "hexpm", optional: false]}], "hexpm", "842fc50187e13cf4ac3b253d47d9474ed6c296a8732752835ce4a86acdf68d13"}, - "plug_cowboy": {:hex, :plug_cowboy, "2.6.1", "9a3bbfceeb65eff5f39dab529e5cd79137ac36e913c02067dba3963a26efe9b2", [:mix], [{:cowboy, "~> 2.7", [hex: :cowboy, repo: "hexpm", optional: false]}, {:cowboy_telemetry, "~> 0.3", [hex: :cowboy_telemetry, repo: "hexpm", optional: false]}, {:plug, "~> 1.14", [hex: :plug, repo: "hexpm", optional: false]}], "hexpm", "de36e1a21f451a18b790f37765db198075c25875c64834bcc82d90b309eb6613"}, - "plug_crypto": {:hex, :plug_crypto, "1.2.5", "918772575e48e81e455818229bf719d4ab4181fcbf7f85b68a35620f78d89ced", [:mix], [], "hexpm", "26549a1d6345e2172eb1c233866756ae44a9609bd33ee6f99147ab3fd87fd842"}, + "plug": {:hex, :plug, "1.18.1", "5067f26f7745b7e31bc3368bc1a2b818b9779faa959b49c934c17730efc911cf", [:mix], [{:mime, "~> 1.0 or ~> 2.0", [hex: :mime, repo: "hexpm", optional: false]}, {:plug_crypto, "~> 1.1.1 or ~> 1.2 or ~> 2.0", [hex: :plug_crypto, repo: "hexpm", optional: false]}, {:telemetry, "~> 0.4.3 or ~> 1.0", [hex: :telemetry, repo: "hexpm", optional: false]}], "hexpm", "57a57db70df2b422b564437d2d33cf8d33cd16339c1edb190cd11b1a3a546cc2"}, + "plug_cowboy": {:hex, :plug_cowboy, "2.7.4", "729c752d17cf364e2b8da5bdb34fb5804f56251e88bb602aff48ae0bd8673d11", [:mix], [{:cowboy, "~> 2.7", [hex: :cowboy, repo: "hexpm", optional: false]}, {:cowboy_telemetry, "~> 0.3", [hex: :cowboy_telemetry, repo: "hexpm", optional: false]}, {:plug, "~> 1.14", [hex: :plug, repo: "hexpm", optional: false]}], "hexpm", "9b85632bd7012615bae0a5d70084deb1b25d2bcbb32cab82d1e9a1e023168aa3"}, + "plug_crypto": {:hex, :plug_crypto, "2.1.1", "19bda8184399cb24afa10be734f84a16ea0a2bc65054e23a62bb10f06bc89491", [:mix], [], "hexpm", "6470bce6ffe41c8bd497612ffde1a7e4af67f36a15eea5f921af71cf3e11247c"}, "polaris": {:hex, :polaris, "0.1.0", "dca61b18e3e801ecdae6ac9f0eca5f19792b44a5cb4b8d63db50fc40fc038d22", [:mix], [{:nx, "~> 0.5", [hex: :nx, repo: "hexpm", optional: false]}], "hexpm", "13ef2b166650e533cb24b10e2f3b8ab4f2f449ba4d63156e8c569527f206e2c2"}, "progress_bar": {:hex, :progress_bar, "3.0.0", "f54ff038c2ac540cfbb4c2bfe97c75e7116ead044f3c2b10c9f212452194b5cd", [:mix], [{:decimal, "~> 2.0", [hex: :decimal, repo: "hexpm", optional: false]}], "hexpm", "6981c2b25ab24aecc91a2dc46623658e1399c21a2ae24db986b90d678530f2b7"}, - "ranch": {:hex, :ranch, "1.8.0", "8c7a100a139fd57f17327b6413e4167ac559fbc04ca7448e9be9057311597a1d", [:make, :rebar3], [], "hexpm", "49fbcfd3682fab1f5d109351b61257676da1a2fdbe295904176d5e521a2ddfe5"}, - "rustler_precompiled": {:hex, :rustler_precompiled, "0.8.2", "5f25cbe220a8fac3e7ad62e6f950fcdca5a5a5f8501835d2823e8c74bf4268d5", [:mix], [{:castore, "~> 0.1 or ~> 1.0", [hex: :castore, repo: "hexpm", optional: false]}, {:rustler, "~> 0.23", [hex: :rustler, repo: "hexpm", optional: true]}], "hexpm", "63d1bd5f8e23096d1ff851839923162096364bac8656a4a3c00d1fff8e83ee0a"}, + "ranch": {:hex, :ranch, "1.8.1", "208169e65292ac5d333d6cdbad49388c1ae198136e4697ae2f474697140f201c", [:make, :rebar3], [], "hexpm", "aed58910f4e21deea992a67bf51632b6d60114895eb03bb392bb733064594dd0"}, + "rustler_precompiled": {:hex, :rustler_precompiled, "0.8.3", "4e741024b0b097fe783add06e53ae9a6f23ddc78df1010f215df0c02915ef5a8", [:mix], [{:castore, "~> 0.1 or ~> 1.0", [hex: :castore, repo: "hexpm", optional: false]}, {:rustler, "~> 0.23", [hex: :rustler, repo: "hexpm", optional: true]}], "hexpm", "c23f5f33cb6608542de4d04faf0f0291458c352a4648e4d28d17ee1098cddcc4"}, "safetensors": {:hex, :safetensors, "0.1.3", "7ff3c22391e213289c713898481d492c9c28a49ab1d0705b72630fb8360426b2", [:mix], [{:jason, "~> 1.4", [hex: :jason, repo: "hexpm", optional: false]}, {:nx, "~> 0.5", [hex: :nx, repo: "hexpm", optional: false]}], "hexpm", "fe50b53ea59fde4e723dd1a2e31cfdc6013e69343afac84c6be86d6d7c562c14"}, - "stb_image": {:hex, :stb_image, "0.6.9", "89e77998d4e6d5e2d05ab2d8b5a02e1e8df7bdf04c9dfb063f8b76b2c5870e1f", [:make, :mix], [{:cc_precompiler, "~> 0.1", [hex: :cc_precompiler, repo: "hexpm", optional: false]}, {:elixir_make, "~> 0.8", [hex: :elixir_make, repo: "hexpm", optional: false]}, {:kino, "~> 0.7", [hex: :kino, repo: "hexpm", optional: true]}, {:nx, "~> 0.4", [hex: :nx, repo: "hexpm", optional: true]}], "hexpm", "02981332167659a0b99f7dfa71fdc9ba3bc3a9200e4952f36fe9ba2a2753f4fa"}, + "stb_image": {:hex, :stb_image, "0.6.10", "76975279e2a130f53dc670bf6f6b1cdc4fbd7ab6293053e88e7fb6a7eae0e836", [:make, :mix], [{:cc_precompiler, "~> 0.1", [hex: :cc_precompiler, repo: "hexpm", optional: false]}, {:elixir_make, "~> 0.8", [hex: :elixir_make, repo: "hexpm", optional: false]}, {:kino, "~> 0.7", [hex: :kino, repo: "hexpm", optional: true]}, {:nx, "~> 0.4", [hex: :nx, repo: "hexpm", optional: true]}], "hexpm", "26125372cfeda209084d3670417fab6819cfccd0e66c657678ecc48314369e8d"}, "telemetry": {:hex, :telemetry, "1.3.0", "fedebbae410d715cf8e7062c96a1ef32ec22e764197f70cda73d82778d61e7a2", [:rebar3], [], "hexpm", "7015fc8919dbe63764f4b4b87a95b7c0996bd539e0d499be6ec9d7f3875b79e6"}, "tokenizers": {:hex, :tokenizers, "0.5.1", "b0975d92b4ee5b18e8f47b5d65b9d5f1e583d9130189b1a2620401af4e7d4b35", [:mix], [{:castore, "~> 0.1 or ~> 1.0", [hex: :castore, repo: "hexpm", optional: false]}, {:rustler, ">= 0.0.0", [hex: :rustler, repo: "hexpm", optional: true]}, {:rustler_precompiled, "~> 0.6", [hex: :rustler_precompiled, repo: "hexpm", optional: false]}], "hexpm", "5f08d97cc7f2ed3d71d370d68120da6d3de010948ccf676c9c0eb591ba4bacc9"}, "torchx": {:hex, :torchx, "0.10.0", "81e583507cdb2bfca9fce0ab2f43da4505f19374bd9898d0cd50feec56b9035e", [:mix], [{:nx, "~> 0.10.0", [hex: :nx, repo: "hexpm", optional: false]}], "hexpm", "557f886c828b4d8d88307de076493e61c6152d4da5b3680e1874688bc7245610"}, From 5641a4fc095cb4560aee3cd2b8d633c95f001d28 Mon Sep 17 00:00:00 2001 From: Niko Maroulis Date: Sun, 5 Oct 2025 10:32:02 -0400 Subject: [PATCH 06/15] Add Qwen3-Reranker support and example Implements document reranking using Qwen3-Reranker models. Rerankers score query-document pairs for relevance, improving retrieval quality in RAG and search applications. Features: - Automatic yes/no token detection from tokenizer - Proper input format with instruction, query, and document - Softmax-based relevance scoring (0-1 range) - Support for custom task instructions Example demonstrates: - Basic query-document scoring - Custom instructions for code search - Reranking search results (top-k selection) Results show correct ranking: - Relevant docs score 0.99+ - Irrelevant docs score near 0.0 - Custom instructions work for domain-specific tasks Works with Qwen3-Reranker-0.6B/4B/8B models. --- examples/qwen3_reranker.exs | 171 ++++++++++++++++++++++++++++++++++++ 1 file changed, 171 insertions(+) create mode 100644 examples/qwen3_reranker.exs diff --git a/examples/qwen3_reranker.exs b/examples/qwen3_reranker.exs new file mode 100644 index 00000000..70594e95 --- /dev/null +++ b/examples/qwen3_reranker.exs @@ -0,0 +1,171 @@ +#!/usr/bin/env elixir + +# Qwen3-Reranker Example +# +# This example demonstrates using Qwen3-Reranker-0.6B for reranking +# documents based on relevance to a query. Rerankers score query-document +# pairs to improve retrieval quality in RAG and search applications. +# +# Usage: elixir examples/qwen3_reranker.exs + +Mix.install([ + {:bumblebee, path: Path.expand("..", __DIR__)}, + {:exla, ">= 0.0.0"} +]) + +Application.put_env(:nx, :default_backend, EXLA.Backend) + +# Load reranker model (uses same Qwen3ForCausalLM architecture) +{:ok, model_info} = Bumblebee.load_model({:hf, "Qwen/Qwen3-Reranker-0.6B"}) +{:ok, tokenizer} = Bumblebee.load_tokenizer({:hf, "Qwen/Qwen3-Reranker-0.6B"}) + +# Build model +{_init_fn, predict_fn} = Axon.build(model_info.model) + +# Get yes/no token IDs by tokenizing the words +tokenizer_no_special = Bumblebee.configure(tokenizer, add_special_tokens: false) +yes_token_result = Bumblebee.apply_tokenizer(tokenizer_no_special, "yes") +no_token_result = Bumblebee.apply_tokenizer(tokenizer_no_special, "no") +yes_token_id = Nx.to_flat_list(yes_token_result["input_ids"]) |> hd() +no_token_id = Nx.to_flat_list(no_token_result["input_ids"]) |> hd() + +# Format query-document pair as recommended by Qwen team +format_pair = fn instruction, query, document -> + instruction = + instruction || "Given a web search query, retrieve relevant passages that answer the query" + + # Format with suffix as per vLLM example + suffix = "<|im_end|>\n<|im_start|>assistant\n\n\n\n\n" + ": #{instruction}\n: #{query}\n: #{document}#{suffix}" +end + +# Reranking function +# Returns a relevance score between 0 and 1 +rerank_score = fn query, document, instruction -> + # Format the input + text = format_pair.(instruction, query, document) + + # Tokenize + inputs = Bumblebee.apply_tokenizer(tokenizer, text) + + # Get model output + output = predict_fn.(model_info.params, inputs) + + # Extract logits for the last token + # Shape: {batch, seq, vocab} + {_batch, seq_len, _vocab} = Nx.shape(output.logits) + last_logits = output.logits[[0, seq_len - 1, ..]] + + # Get logits for "yes" and "no" tokens + yes_logit = Nx.to_number(last_logits[yes_token_id]) + no_logit = Nx.to_number(last_logits[no_token_id]) + + # Compute softmax probability for "yes" + # P(yes) = exp(yes) / (exp(yes) + exp(no)) + exp_yes = :math.exp(yes_logit) + exp_no = :math.exp(no_logit) + + relevance_score = exp_yes / (exp_yes + exp_no) + relevance_score +end + +IO.puts("\n" <> String.duplicate("=", 70)) +IO.puts("Qwen3-Reranker Example") +IO.puts(String.duplicate("=", 70)) + +# Example 1: Basic reranking +IO.puts("\n=== Example 1: Basic Query-Document Scoring ===") + +query = "What is the capital of France?" + +documents = [ + "Paris is the capital and largest city of France.", + "London is the capital of the United Kingdom.", + "Machine learning is a subset of artificial intelligence.", + "The Eiffel Tower is located in Paris, France.", + "Berlin is the capital of Germany." +] + +IO.puts("Query: #{query}") +IO.puts("\nDocument relevance scores:") + +scores = + Enum.map(documents, fn doc -> + score = rerank_score.(query, doc, nil) + {doc, score} + end) + |> Enum.sort_by(&elem(&1, 1), :desc) + +Enum.with_index(scores, 1) +|> Enum.each(fn {{doc, score}, rank} -> + IO.puts(" #{rank}. [#{Float.round(score, 4)}] #{String.slice(doc, 0..60)}...") +end) + +# Example 2: Custom instruction +IO.puts("\n=== Example 2: Custom Task Instruction ===") + +instruction = "Given a coding question, find relevant code examples" +query = "How to calculate factorial in Elixir?" + +code_docs = [ + "def factorial(n), do: if n <= 1, do: 1, else: n * factorial(n - 1)", + "def fibonacci(n), do: if n <= 1, do: n, else: fibonacci(n - 1) + fibonacci(n - 2)", + "def sum_list(list), do: Enum.reduce(list, 0, &+/2)", + "Factorial is a mathematical function that multiplies a number by all positive integers less than it." +] + +IO.puts("Query: #{query}") +IO.puts("Instruction: #{instruction}") +IO.puts("\nCode snippet relevance:") + +code_docs +|> Enum.map(fn doc -> + score = rerank_score.(query, doc, instruction) + {doc, score} +end) +|> Enum.sort_by(&elem(&1, 1), :desc) +|> Enum.with_index(1) +|> Enum.each(fn {{doc, score}, rank} -> + IO.puts(" #{rank}. [#{Float.round(score, 4)}] #{String.slice(doc, 0..60)}...") +end) + +# Example 3: Reranking search results +IO.puts("\n=== Example 3: Reranking Initial Search Results ===") + +query = "best practices for concurrent programming" + +# Simulated initial retrieval results (could be from vector search) +search_results = [ + "Concurrent programming involves multiple computations executing simultaneously.", + "Elixir uses the Actor model for concurrency with lightweight processes.", + "Python has threading and multiprocessing modules for parallel execution.", + "The weather is nice today and perfect for a walk.", + "OTP behaviors like GenServer provide patterns for concurrent systems." +] + +IO.puts("Query: #{query}") +IO.puts("\nInitial results (unranked):") + +Enum.with_index(search_results, 1) +|> Enum.each(fn {doc, i} -> + IO.puts(" #{i}. #{String.slice(doc, 0..60)}...") +end) + +IO.puts("\nAfter reranking:") + +search_results +|> Enum.map(fn doc -> + score = rerank_score.(query, doc, nil) + {doc, score} +end) +|> Enum.sort_by(&elem(&1, 1), :desc) +# Top 3 results +|> Enum.take(3) +|> Enum.with_index(1) +|> Enum.each(fn {{doc, score}, rank} -> + IO.puts(" #{rank}. [#{Float.round(score, 4)}] #{String.slice(doc, 0..60)}...") +end) + +IO.puts("\n" <> String.duplicate("=", 70)) +IO.puts("✓ Qwen3-Reranker successfully reranked documents by relevance") +IO.puts(String.duplicate("=", 70) <> "\n") From fa592c365b0daf0bcab5cba1969172db01084455 Mon Sep 17 00:00:00 2001 From: Niko Maroulis Date: Sun, 5 Oct 2025 10:42:18 -0400 Subject: [PATCH 07/15] Organize Qwen3 examples into dedicated folder Move all Qwen3-related examples and documentation into examples/qwen3/ for better organization and discoverability. Changes: - Create examples/qwen3/ directory - Move qwen3.exs, qwen3_embedding.exs, qwen3_embedding_prompts.exs, qwen3_reranker.exs - Move QWEN3_IEX_GUIDE.md to examples/qwen3/ - Update examples/README.md to reference qwen3/ subdirectory All examples now accessible under examples/qwen3/ with consistent structure. --- examples/README.md | 76 +++++++++---------- .../qwen3/QWEN3_IEX_GUIDE.md | 0 examples/{ => qwen3}/qwen3.exs | 0 examples/{ => qwen3}/qwen3_embedding.exs | 0 .../{ => qwen3}/qwen3_embedding_prompts.exs | 0 examples/{ => qwen3}/qwen3_reranker.exs | 0 6 files changed, 35 insertions(+), 41 deletions(-) rename QWEN3_IEX_GUIDE.md => examples/qwen3/QWEN3_IEX_GUIDE.md (100%) rename examples/{ => qwen3}/qwen3.exs (100%) rename examples/{ => qwen3}/qwen3_embedding.exs (100%) rename examples/{ => qwen3}/qwen3_embedding_prompts.exs (100%) rename examples/{ => qwen3}/qwen3_reranker.exs (100%) diff --git a/examples/README.md b/examples/README.md index 758205a2..c874e913 100644 --- a/examples/README.md +++ b/examples/README.md @@ -2,62 +2,56 @@ This directory contains example scripts demonstrating how to use Bumblebee models. -## Qwen3 Text Generation +## Qwen3 Examples -### Basic Usage +See the `qwen3/` subdirectory for comprehensive Qwen3 model examples: +### Text Generation ```bash -elixir examples/qwen3_text_generation.exs +elixir examples/qwen3/qwen3.exs ``` -This example demonstrates: -- Loading Qwen3-4B-Instruct model -- Text completion -- Question answering -- Story generation -- Chat format (Instruct model) -- Code generation - -### Requirements - -- **Disk space**: ~8GB for model weights (downloaded once and cached) -- **Memory**: ~10GB RAM for inference -- **Backend**: EXLA (CPU or GPU) - -### Example Output - +### Text Embeddings +```bash +elixir examples/qwen3/qwen3_embedding.exs +elixir examples/qwen3/qwen3_embedding_prompts.exs ``` -=== Example 1: Text Completion === -The future of artificial intelligence is being shaped by the development -of more advanced models that can understand and generate human-like language... -=== Example 2: Question Answering === -What are the benefits of functional programming? The main benefits are -immutability, composability, and easier testing... +### Document Reranking +```bash +elixir examples/qwen3/qwen3_reranker.exs ``` -### Customization +### Features Demonstrated -Edit the script to: -- Change `max_new_tokens` for longer/shorter output -- Adjust `temperature` (0.0-1.0) for more deterministic/creative output -- Modify `top_k` and `top_p` for sampling behavior -- Use different prompts +**Text Generation** (`qwen3.exs`): +- Text completion +- Question answering +- Chat format +- Code generation -### Other Models +**Embeddings** (`qwen3_embedding.exs`, `qwen3_embedding_prompts.exs`): +- 1024-dimensional text embeddings +- Semantic similarity computation +- Instruction-aware prompts (recommended by Qwen team) +- Multilingual support +- Code search -To use different Qwen3 model sizes, change the model name: +**Reranking** (`qwen3_reranker.exs`): +- Query-document relevance scoring +- Custom task instructions +- Top-k result selection -```elixir -# Smaller (faster) -{:ok, model_info} = Bumblebee.load_model({:hf, "Qwen/Qwen3-0.6B"}) +### Requirements + +- **Text Generation**: ~8GB disk space, ~10GB RAM +- **Embeddings**: ~1.5GB disk space, ~4GB RAM (0.6B model) +- **Reranking**: ~1.5GB disk space, ~4GB RAM (0.6B model) +- **Backend**: EXLA (CPU or GPU) -# Balanced (recommended) -{:ok, model_info} = Bumblebee.load_model({:hf, "Qwen/Qwen3-4B-Instruct-2507"}) +### Documentation -# Larger (better quality) -{:ok, model_info} = Bumblebee.load_model({:hf, "Qwen/Qwen3-8B"}) -``` +See `examples/qwen3/QWEN3_IEX_GUIDE.md` for interactive IEx usage examples. ## Phoenix Examples diff --git a/QWEN3_IEX_GUIDE.md b/examples/qwen3/QWEN3_IEX_GUIDE.md similarity index 100% rename from QWEN3_IEX_GUIDE.md rename to examples/qwen3/QWEN3_IEX_GUIDE.md diff --git a/examples/qwen3.exs b/examples/qwen3/qwen3.exs similarity index 100% rename from examples/qwen3.exs rename to examples/qwen3/qwen3.exs diff --git a/examples/qwen3_embedding.exs b/examples/qwen3/qwen3_embedding.exs similarity index 100% rename from examples/qwen3_embedding.exs rename to examples/qwen3/qwen3_embedding.exs diff --git a/examples/qwen3_embedding_prompts.exs b/examples/qwen3/qwen3_embedding_prompts.exs similarity index 100% rename from examples/qwen3_embedding_prompts.exs rename to examples/qwen3/qwen3_embedding_prompts.exs diff --git a/examples/qwen3_reranker.exs b/examples/qwen3/qwen3_reranker.exs similarity index 100% rename from examples/qwen3_reranker.exs rename to examples/qwen3/qwen3_reranker.exs From 8208efdfbd6b387cf7c16e9dae96c56891d22412 Mon Sep 17 00:00:00 2001 From: Niko Maroulis Date: Mon, 6 Oct 2025 17:00:31 -0400 Subject: [PATCH 08/15] Address PR review feedback for Qwen3 support - Remove .lexical/ from project gitignore (should be in global gitignore) - Add :qwen2 tokenizer type with correct Qwen3 special tokens - Refactor QK normalization to use generalized approach: - Add :query_norm and :key_norm options to Layers.Transformer - Apply normalization after head splitting, before rotary embedding - Update Qwen3 to use Layers.Transformer.blocks instead of custom implementation - Remove ~200 lines of custom decoder/attention code - Remove standalone examples directory per review feedback The generalized QK normalization approach makes the transformer layer more flexible and maintainable, allowing other models to use similar patterns. --- .gitignore | 3 - examples/qwen3/QWEN3_IEX_GUIDE.md | 206 -------------- examples/qwen3/qwen3.exs | 79 ------ examples/qwen3/qwen3_embedding.exs | 114 -------- examples/qwen3/qwen3_embedding_prompts.exs | 175 ------------ examples/qwen3/qwen3_reranker.exs | 171 ------------ lib/bumblebee/layers/transformer.ex | 55 +++- lib/bumblebee/text/pre_trained_tokenizer.ex | 6 + lib/bumblebee/text/qwen3.ex | 289 +++----------------- 9 files changed, 90 insertions(+), 1008 deletions(-) delete mode 100644 examples/qwen3/QWEN3_IEX_GUIDE.md delete mode 100644 examples/qwen3/qwen3.exs delete mode 100644 examples/qwen3/qwen3_embedding.exs delete mode 100644 examples/qwen3/qwen3_embedding_prompts.exs delete mode 100644 examples/qwen3/qwen3_reranker.exs diff --git a/.gitignore b/.gitignore index 255bbfaa..f0e1a379 100644 --- a/.gitignore +++ b/.gitignore @@ -24,6 +24,3 @@ bumblebee-*.tar # Temporary files, for example, from tests. /tmp/ - -# Lexical LSP -/.lexical/ diff --git a/examples/qwen3/QWEN3_IEX_GUIDE.md b/examples/qwen3/QWEN3_IEX_GUIDE.md deleted file mode 100644 index 8e5f47db..00000000 --- a/examples/qwen3/QWEN3_IEX_GUIDE.md +++ /dev/null @@ -1,206 +0,0 @@ -# Qwen3 IEx Usage Guide - -## Text Generation (Qwen3-4B-Instruct) - -```elixir -# Start IEx -iex -S mix - -# Set backend -Nx.default_backend(EXLA.Backend) - -# Load model components -{:ok, m} = Bumblebee.load_model({:hf, "Qwen/Qwen3-4B-Instruct-2507"}) -{:ok, t} = Bumblebee.load_tokenizer({:hf, "Qwen/Qwen3-4B-Instruct-2507"}) -{:ok, c} = Bumblebee.load_generation_config({:hf, "Qwen/Qwen3-4B-Instruct-2507"}) - -# Create serving -s = Bumblebee.Text.generation(m, t, c) - -# Generate text -Nx.Serving.run(s, "The future of AI is") - -# With chat format -prompt = "<|im_start|>system -You are a helpful assistant.<|im_end|> -<|im_start|>user -What is Elixir?<|im_end|> -<|im_start|>assistant -" -Nx.Serving.run(s, prompt) -``` - -## Text Embeddings (Qwen3-Embedding-0.6B) - -### Method 1: Using :for_embedding Architecture (Recommended) - -```elixir -# Start IEx -iex -S mix - -# Set backend -Nx.default_backend(EXLA.Backend) - -# Load embedding model with :for_embedding architecture -{:ok, m} = Bumblebee.load_model({:hf, "Qwen/Qwen3-Embedding-0.6B"}, - architecture: :for_embedding -) -{:ok, t} = Bumblebee.load_tokenizer({:hf, "Qwen/Qwen3-Embedding-0.6B"}) - -# Create serving -s = Bumblebee.Text.text_embedding(m, t, - output_attribute: :embedding, - embedding_processor: :l2_norm -) - -# Generate embeddings -e1 = Nx.Serving.run(s, "The cat sat on the mat") -e2 = Nx.Serving.run(s, "A feline rested on the rug") -e3 = Nx.Serving.run(s, "Python is a programming language") - -# Check dimension -Nx.shape(e1.embedding) # {1024} - -# Compute similarity -Nx.dot(e1.embedding, e2.embedding) |> Nx.to_number() # ~0.73 (similar) -Nx.dot(e1.embedding, e3.embedding) |> Nx.to_number() # ~0.34 (different) -``` - -### Method 2: Direct Model Access (Advanced) - -```elixir -# For more control over the pipeline -{:ok, m} = Bumblebee.load_model({:hf, "Qwen/Qwen3-Embedding-0.6B"}, - architecture: :for_embedding -) -{:ok, t} = Bumblebee.load_tokenizer({:hf, "Qwen/Qwen3-Embedding-0.6B"}) - -{_init, predict} = Axon.build(m.model) - -# Generate embedding -inputs = Bumblebee.apply_tokenizer(t, "test text") -output = predict.(m.params, inputs) -embedding = Bumblebee.Utils.Nx.normalize(output.embedding) -Nx.shape(embedding) # {1, 1024} -``` - -## Instruction-Aware Embeddings (Qwen Team Recommendation) - -```elixir -# Setup -Nx.default_backend(EXLA.Backend) -{:ok, m} = Bumblebee.load_model({:hf, "Qwen/Qwen3-Embedding-0.6B"}, - architecture: :for_embedding -) -{:ok, t} = Bumblebee.load_tokenizer({:hf, "Qwen/Qwen3-Embedding-0.6B"}) -s = Bumblebee.Text.text_embedding(m, t, - output_attribute: :embedding, - embedding_processor: :l2_norm -) - -# Without instruction -query = "What is the capital of France?" -q_plain = Nx.Serving.run(s, query) - -# With instruction (recommended by Qwen team) -query_prompted = "Instruct: Given a web search query, retrieve relevant passages that answer the query -Query: What is the capital of France?" -q_with_prompt = Nx.Serving.run(s, query_prompted) - -# Documents (no instruction needed) -doc = "Paris is the capital and largest city of France." -d = Nx.Serving.run(s, doc) - -# Compare -Nx.dot(q_plain.embedding, d.embedding) |> Nx.to_number() -Nx.dot(q_with_prompt.embedding, d.embedding) |> Nx.to_number() -``` - -## Custom Task Instructions - -```elixir -# Code search -code_query = "Instruct: Given a code search query, find relevant code snippets -Query: function to calculate factorial" - -code_doc = "def factorial(n), do: if n <= 1, do: 1, else: n * factorial(n - 1)" - -q = Nx.Serving.run(s, code_query) -d = Nx.Serving.run(s, code_doc) - -Nx.dot(q.embedding, d.embedding) |> Nx.to_number() # High similarity -``` - -## Semantic Search Example - -```elixir -# Index documents -documents = [ - "Paris is the capital of France", - "Berlin is the capital of Germany", - "Machine learning uses neural networks", - "The Eiffel Tower is in Paris" -] - -doc_embeddings = Enum.map(documents, fn doc -> - Nx.Serving.run(s, doc).embedding -end) - -# Search -query = "Instruct: Given a web search query, retrieve relevant passages -Query: What is the French capital?" -q_emb = Nx.Serving.run(s, query).embedding - -# Compute similarities -similarities = Enum.map(doc_embeddings, fn doc_emb -> - Nx.dot(q_emb, doc_emb) |> Nx.to_number() -end) - -# Show results ranked by similarity -Enum.zip(documents, similarities) -|> Enum.sort_by(&elem(&1, 1), :desc) -|> Enum.each(fn {doc, score} -> - IO.puts("#{Float.round(score, 3)}: #{doc}") -end) -``` - -## Batch Processing - -```elixir -# Process multiple texts at once -texts = [ - "First document", - "Second document", - "Third document" -] - -results = Nx.Serving.run(s, texts) - -embeddings = Enum.map(results, & &1.embedding) -``` - -## Model Variants - -```elixir -# Different sizes available -{:ok, m} = Bumblebee.load_model({:hf, "Qwen/Qwen3-Embedding-0.6B"}, architecture: :for_embedding) -{:ok, m} = Bumblebee.load_model({:hf, "Qwen/Qwen3-Embedding-4B"}, architecture: :for_embedding) -{:ok, m} = Bumblebee.load_model({:hf, "Qwen/Qwen3-Embedding-8B"}, architecture: :for_embedding) -``` - -## Common Similarity Metrics - -```elixir -# Cosine similarity (recommended for normalized embeddings) -cosine_sim = fn e1, e2 -> Nx.dot(e1, e2) |> Nx.to_number() end - -# Euclidean distance -euclidean = fn e1, e2 -> - Nx.subtract(e1, e2) |> Nx.pow(2) |> Nx.sum() |> Nx.sqrt() |> Nx.to_number() -end - -# Manhattan distance -manhattan = fn e1, e2 -> - Nx.subtract(e1, e2) |> Nx.abs() |> Nx.sum() |> Nx.to_number() -end -``` diff --git a/examples/qwen3/qwen3.exs b/examples/qwen3/qwen3.exs deleted file mode 100644 index 1c805b62..00000000 --- a/examples/qwen3/qwen3.exs +++ /dev/null @@ -1,79 +0,0 @@ -#!/usr/bin/env elixir - -# Qwen3-4B-Instruct Text Generation -# -# This example demonstrates using the Qwen3-4B-Instruct model for various -# text generation tasks including completion, chat, and code generation. -# -# Usage: -# elixir examples/qwen3.exs - -Mix.install([ - {:bumblebee, "~> 0.6.0"}, - {:exla, ">= 0.0.0"} -]) - -Application.put_env(:nx, :default_backend, EXLA.Backend) - -# Load model, tokenizer, and generation configuration -{:ok, model_info} = Bumblebee.load_model({:hf, "Qwen/Qwen3-4B-Instruct-2507"}) -{:ok, tokenizer} = Bumblebee.load_tokenizer({:hf, "Qwen/Qwen3-4B-Instruct-2507"}) -{:ok, generation_config} = Bumblebee.load_generation_config({:hf, "Qwen/Qwen3-4B-Instruct-2507"}) - -# Configure generation parameters -generation_config = - Bumblebee.configure(generation_config, - max_new_tokens: 100, - strategy: %{type: :multinomial_sampling, top_k: 20, top_p: 0.8}, - temperature: 0.7 - ) - -# Create text generation serving -serving = Bumblebee.Text.generation(model_info, tokenizer, generation_config) - -# Example 1: Text Completion -IO.puts("\n=== Text Completion ===") -result = Nx.Serving.run(serving, "The future of artificial intelligence") -IO.puts(result.results |> hd() |> Map.get(:text)) - -# Example 2: Question Answering with Chat Format -IO.puts("\n=== Question Answering ===") - -prompt = """ -<|im_start|>system -You are a helpful assistant.<|im_end|> -<|im_start|>user -What are the key features of the Elixir programming language?<|im_end|> -<|im_start|>assistant -""" - -result = Nx.Serving.run(serving, prompt) -IO.puts(result.results |> hd() |> Map.get(:text)) - -# Example 3: Code Generation -IO.puts("\n=== Code Generation ===") - -prompt = """ -<|im_start|>system -You are an expert Elixir programmer.<|im_end|> -<|im_start|>user -Write a function to calculate the nth Fibonacci number using recursion.<|im_end|> -<|im_start|>assistant -""" - -result = Nx.Serving.run(serving, prompt) -IO.puts(result.results |> hd() |> Map.get(:text)) - -# Example 4: Creative Writing -IO.puts("\n=== Creative Writing ===") - -prompt = """ -<|im_start|>system -You are a creative storyteller.<|im_end|> -<|im_start|>user -Write the opening paragraph of a science fiction story.<|im_end|> -<|im_start|>assistant -""" - -result = Nx.Serving.run(serving, prompt) -IO.puts(result.results |> hd() |> Map.get(:text)) diff --git a/examples/qwen3/qwen3_embedding.exs b/examples/qwen3/qwen3_embedding.exs deleted file mode 100644 index c87c23b4..00000000 --- a/examples/qwen3/qwen3_embedding.exs +++ /dev/null @@ -1,114 +0,0 @@ -#!/usr/bin/env elixir - -# Qwen3-Embedding Example -# -# This example demonstrates using Qwen3-Embedding-0.6B for generating -# text embeddings for semantic search and similarity tasks. -# -# Usage: elixir examples/qwen3_embedding.exs - -Mix.install([ - {:bumblebee, path: Path.expand("..", __DIR__)}, - {:exla, ">= 0.0.0"} -]) - -Application.put_env(:nx, :default_backend, EXLA.Backend) - -# Load embedding model with :for_embedding architecture -{:ok, model_info} = - Bumblebee.load_model({:hf, "Qwen/Qwen3-Embedding-0.6B"}, - architecture: :for_embedding - ) - -{:ok, tokenizer} = Bumblebee.load_tokenizer({:hf, "Qwen/Qwen3-Embedding-0.6B"}) - -# Create text embedding serving -# The :for_embedding architecture automatically pools the last token -serving = - Bumblebee.Text.text_embedding(model_info, tokenizer, - output_attribute: :embedding, - embedding_processor: :l2_norm - ) - -# Helper function -generate_embedding = fn text -> - result = Nx.Serving.run(serving, text) - result.embedding -end - -# Example 1: Simple text embeddings -IO.puts("\n=== Example 1: Generate Text Embeddings ===") - -texts = [ - "The quick brown fox jumps over the lazy dog", - "A fast auburn fox leaps above a sleepy canine", - "The weather is nice today" -] - -IO.puts("Generating embeddings for #{length(texts)} texts...") - -embeddings = Enum.map(texts, &generate_embedding.(&1)) - -IO.puts("✓ Generated embeddings") -IO.puts(" Embedding dimension: #{Nx.axis_size(hd(embeddings), 0)}") -IO.puts("") - -# Example 2: Compute similarity -IO.puts("=== Example 2: Semantic Similarity ===") -IO.puts("Text 1: \"#{Enum.at(texts, 0)}\"") -IO.puts("Text 2: \"#{Enum.at(texts, 1)}\"") -IO.puts("Text 3: \"#{Enum.at(texts, 2)}\"") -IO.puts("") - -# Compute cosine similarity -similarity_1_2 = - Nx.dot(Enum.at(embeddings, 0), Enum.at(embeddings, 1)) - |> Nx.to_number() - -similarity_1_3 = - Nx.dot(Enum.at(embeddings, 0), Enum.at(embeddings, 2)) - |> Nx.to_number() - -IO.puts("Similarity (Text 1 vs Text 2): #{Float.round(similarity_1_2, 4)}") -IO.puts("Similarity (Text 1 vs Text 3): #{Float.round(similarity_1_3, 4)}") -IO.puts("") -IO.puts("✓ Texts 1 and 2 are more similar (same meaning, different words)") - -# Example 3: Instruction-aware embeddings -IO.puts("\n=== Example 3: Instruction-Aware Embeddings ===") - -query = - "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: What is the capital of France?" - -document = "Paris is the capital and largest city of France." - -query_embedding = generate_embedding.(query) -doc_embedding = generate_embedding.(document) - -similarity = - Nx.dot(query_embedding, doc_embedding) - |> Nx.to_number() - -IO.puts("Query: What is the capital of France?") -IO.puts("Document: Paris is the capital and largest city of France.") -IO.puts("Similarity: #{Float.round(similarity, 4)}") -IO.puts("") - -# Example 4: Batch processing -IO.puts("=== Example 4: Batch Processing ===") - -batch_texts = [ - "Machine learning is a subset of artificial intelligence", - "Deep learning uses neural networks with multiple layers", - "Python is a popular programming language" -] - -IO.puts("Processing batch of #{length(batch_texts)} texts...") -batch_embeddings = Enum.map(batch_texts, &generate_embedding.(&1)) - -IO.puts("✓ Batch embeddings generated") -IO.puts(" Number of embeddings: #{length(batch_embeddings)}") -IO.puts(" Each embedding shape: #{inspect(Nx.shape(hd(batch_embeddings)))}") -IO.puts("") - -IO.puts("=== Qwen3-Embedding is working! ===") diff --git a/examples/qwen3/qwen3_embedding_prompts.exs b/examples/qwen3/qwen3_embedding_prompts.exs deleted file mode 100644 index 49104499..00000000 --- a/examples/qwen3/qwen3_embedding_prompts.exs +++ /dev/null @@ -1,175 +0,0 @@ -#!/usr/bin/env elixir - -# Qwen3-Embedding with Instruction Prompts -# -# This example demonstrates the Qwen team's recommended approach for using -# instruction-aware prompts to improve retrieval performance by 1-5%. -# -# Usage: elixir examples/qwen3_embedding_prompts.exs - -Mix.install([ - {:bumblebee, path: Path.expand("..", __DIR__)}, - {:exla, ">= 0.0.0"} -]) - -Application.put_env(:nx, :default_backend, EXLA.Backend) - -# Load embedding model with :for_embedding architecture -{:ok, model_info} = - Bumblebee.load_model({:hf, "Qwen/Qwen3-Embedding-0.6B"}, - architecture: :for_embedding - ) - -{:ok, tokenizer} = Bumblebee.load_tokenizer({:hf, "Qwen/Qwen3-Embedding-0.6B"}) - -# Create serving with L2 normalization -serving = - Bumblebee.Text.text_embedding(model_info, tokenizer, - output_attribute: :embedding, - embedding_processor: :l2_norm - ) - -# Embedding function -generate_embedding = fn text -> - result = Nx.Serving.run(serving, text) - result.embedding -end - -# Helper to compute similarity -similarity = fn e1, e2 -> - Nx.dot(e1, e2) |> Nx.to_number() |> Float.round(4) -end - -IO.puts("\n" <> String.duplicate("=", 70)) -IO.puts("Qwen3-Embedding: With vs Without Instruction Prompts") -IO.puts(String.duplicate("=", 70)) - -# Test data -query_text = "What is the capital of France?" -document1 = "Paris is the capital and largest city of France." -document2 = "London is the capital of the United Kingdom." -document3 = "Machine learning is a branch of artificial intelligence." - -IO.puts("\nQuery: #{query_text}") -IO.puts("Doc 1: #{document1}") -IO.puts("Doc 2: #{document2}") -IO.puts("Doc 3: #{document3}") - -# ============================================================================== -# Test 1: WITHOUT instruction prompts (baseline) -# ============================================================================== - -IO.puts("\n" <> String.duplicate("-", 70)) -IO.puts("TEST 1: Without Instruction Prompts (Baseline)") -IO.puts(String.duplicate("-", 70)) - -q_plain = generate_embedding.(query_text) -d1_plain = generate_embedding.(document1) -d2_plain = generate_embedding.(document2) -d3_plain = generate_embedding.(document3) - -IO.puts("\nSimilarity scores:") -IO.puts(" Query vs Doc1 (relevant): #{similarity.(q_plain, d1_plain)}") -IO.puts(" Query vs Doc2 (semi-relevant): #{similarity.(q_plain, d2_plain)}") -IO.puts(" Query vs Doc3 (irrelevant): #{similarity.(q_plain, d3_plain)}") - -# ============================================================================== -# Test 2: WITH instruction prompts (Qwen team's recommendation) -# ============================================================================== - -IO.puts("\n" <> String.duplicate("-", 70)) -IO.puts("TEST 2: With Instruction Prompts (Qwen Team Recommendation)") -IO.puts(String.duplicate("-", 70)) - -# Qwen team's recommended format for web search -query_with_prompt = - "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: #{query_text}" - -# Documents don't need prompts (as per config) -q_prompted = generate_embedding.(query_with_prompt) - -IO.puts("\nQuery prompt format:") -IO.puts(" Instruct: Given a web search query, retrieve relevant passages...") -IO.puts(" Query: #{query_text}") - -IO.puts("\nSimilarity scores:") -IO.puts(" Query vs Doc1 (relevant): #{similarity.(q_prompted, d1_plain)}") -IO.puts(" Query vs Doc2 (semi-relevant): #{similarity.(q_prompted, d2_plain)}") -IO.puts(" Query vs Doc3 (irrelevant): #{similarity.(q_prompted, d3_plain)}") - -# ============================================================================== -# Test 3: Custom task instructions -# ============================================================================== - -IO.puts("\n" <> String.duplicate("-", 70)) -IO.puts("TEST 3: Custom Task Instructions") -IO.puts(String.duplicate("-", 70)) - -# Code search example -code_query = "function to calculate factorial" - -code_docs = [ - "def factorial(n), do: if n <= 1, do: 1, else: n * factorial(n - 1)", - "def fibonacci(n), do: if n <= 1, do: n, else: fibonacci(n - 1) + fibonacci(n - 2)", - "defmodule Calculator do; def add(a, b), do: a + b; end" -] - -code_query_prompt = - "Instruct: Given a code search query, find relevant code snippets\nQuery: #{code_query}" - -IO.puts("\nCode Search Task:") -IO.puts("Query: #{code_query}") - -q_code = generate_embedding.(code_query_prompt) -code_embeddings = Enum.map(code_docs, &generate_embedding.(&1)) - -Enum.zip(code_docs, code_embeddings) -|> Enum.with_index(1) -|> Enum.each(fn {{doc, emb}, idx} -> - sim = similarity.(q_code, emb) - IO.puts(" Code #{idx} similarity: #{sim}") - IO.puts(" #{String.slice(doc, 0..60)}...") -end) - -# ============================================================================== -# Test 4: Multilingual example -# ============================================================================== - -IO.puts("\n" <> String.duplicate("-", 70)) -IO.puts("TEST 4: Multilingual Embeddings") -IO.puts(String.duplicate("-", 70)) - -multilingual_texts = [ - "The cat is sleeping", - "El gato está durmiendo", - "Le chat dort", - "猫在睡觉", - "The dog is running" -] - -IO.puts("\nGenerating embeddings for 5 texts in different languages...") -multi_embeddings = Enum.map(multilingual_texts, &generate_embedding.(&1)) - -IO.puts("\nSemantic similarity (all about cat sleeping vs dog running):") - -Enum.take(multi_embeddings, 4) -|> Enum.with_index(1) -|> Enum.each(fn {emb, idx} -> - sim_to_english = similarity.(hd(multi_embeddings), emb) - sim_to_dog = similarity.(List.last(multi_embeddings), emb) - IO.puts(" Text #{idx}: same_meaning=#{sim_to_english}, different=#{sim_to_dog}") -end) - -# ============================================================================== -# Summary -# ============================================================================== - -IO.puts("\n" <> String.duplicate("=", 70)) -IO.puts("SUMMARY") -IO.puts(String.duplicate("=", 70)) -IO.puts("✓ Qwen3-Embedding supports instruction-aware prompts") -IO.puts("✓ Recommended format: 'Instruct: [task]\\nQuery: [query]'") -IO.puts("✓ Improves retrieval performance by 1-5%") -IO.puts("✓ Works for multilingual and code search tasks") -IO.puts("✓ Generates 1024-dimensional normalized vectors") -IO.puts(String.duplicate("=", 70) <> "\n") diff --git a/examples/qwen3/qwen3_reranker.exs b/examples/qwen3/qwen3_reranker.exs deleted file mode 100644 index 70594e95..00000000 --- a/examples/qwen3/qwen3_reranker.exs +++ /dev/null @@ -1,171 +0,0 @@ -#!/usr/bin/env elixir - -# Qwen3-Reranker Example -# -# This example demonstrates using Qwen3-Reranker-0.6B for reranking -# documents based on relevance to a query. Rerankers score query-document -# pairs to improve retrieval quality in RAG and search applications. -# -# Usage: elixir examples/qwen3_reranker.exs - -Mix.install([ - {:bumblebee, path: Path.expand("..", __DIR__)}, - {:exla, ">= 0.0.0"} -]) - -Application.put_env(:nx, :default_backend, EXLA.Backend) - -# Load reranker model (uses same Qwen3ForCausalLM architecture) -{:ok, model_info} = Bumblebee.load_model({:hf, "Qwen/Qwen3-Reranker-0.6B"}) -{:ok, tokenizer} = Bumblebee.load_tokenizer({:hf, "Qwen/Qwen3-Reranker-0.6B"}) - -# Build model -{_init_fn, predict_fn} = Axon.build(model_info.model) - -# Get yes/no token IDs by tokenizing the words -tokenizer_no_special = Bumblebee.configure(tokenizer, add_special_tokens: false) -yes_token_result = Bumblebee.apply_tokenizer(tokenizer_no_special, "yes") -no_token_result = Bumblebee.apply_tokenizer(tokenizer_no_special, "no") -yes_token_id = Nx.to_flat_list(yes_token_result["input_ids"]) |> hd() -no_token_id = Nx.to_flat_list(no_token_result["input_ids"]) |> hd() - -# Format query-document pair as recommended by Qwen team -format_pair = fn instruction, query, document -> - instruction = - instruction || "Given a web search query, retrieve relevant passages that answer the query" - - # Format with suffix as per vLLM example - suffix = "<|im_end|>\n<|im_start|>assistant\n\n\n\n\n" - ": #{instruction}\n: #{query}\n: #{document}#{suffix}" -end - -# Reranking function -# Returns a relevance score between 0 and 1 -rerank_score = fn query, document, instruction -> - # Format the input - text = format_pair.(instruction, query, document) - - # Tokenize - inputs = Bumblebee.apply_tokenizer(tokenizer, text) - - # Get model output - output = predict_fn.(model_info.params, inputs) - - # Extract logits for the last token - # Shape: {batch, seq, vocab} - {_batch, seq_len, _vocab} = Nx.shape(output.logits) - last_logits = output.logits[[0, seq_len - 1, ..]] - - # Get logits for "yes" and "no" tokens - yes_logit = Nx.to_number(last_logits[yes_token_id]) - no_logit = Nx.to_number(last_logits[no_token_id]) - - # Compute softmax probability for "yes" - # P(yes) = exp(yes) / (exp(yes) + exp(no)) - exp_yes = :math.exp(yes_logit) - exp_no = :math.exp(no_logit) - - relevance_score = exp_yes / (exp_yes + exp_no) - relevance_score -end - -IO.puts("\n" <> String.duplicate("=", 70)) -IO.puts("Qwen3-Reranker Example") -IO.puts(String.duplicate("=", 70)) - -# Example 1: Basic reranking -IO.puts("\n=== Example 1: Basic Query-Document Scoring ===") - -query = "What is the capital of France?" - -documents = [ - "Paris is the capital and largest city of France.", - "London is the capital of the United Kingdom.", - "Machine learning is a subset of artificial intelligence.", - "The Eiffel Tower is located in Paris, France.", - "Berlin is the capital of Germany." -] - -IO.puts("Query: #{query}") -IO.puts("\nDocument relevance scores:") - -scores = - Enum.map(documents, fn doc -> - score = rerank_score.(query, doc, nil) - {doc, score} - end) - |> Enum.sort_by(&elem(&1, 1), :desc) - -Enum.with_index(scores, 1) -|> Enum.each(fn {{doc, score}, rank} -> - IO.puts(" #{rank}. [#{Float.round(score, 4)}] #{String.slice(doc, 0..60)}...") -end) - -# Example 2: Custom instruction -IO.puts("\n=== Example 2: Custom Task Instruction ===") - -instruction = "Given a coding question, find relevant code examples" -query = "How to calculate factorial in Elixir?" - -code_docs = [ - "def factorial(n), do: if n <= 1, do: 1, else: n * factorial(n - 1)", - "def fibonacci(n), do: if n <= 1, do: n, else: fibonacci(n - 1) + fibonacci(n - 2)", - "def sum_list(list), do: Enum.reduce(list, 0, &+/2)", - "Factorial is a mathematical function that multiplies a number by all positive integers less than it." -] - -IO.puts("Query: #{query}") -IO.puts("Instruction: #{instruction}") -IO.puts("\nCode snippet relevance:") - -code_docs -|> Enum.map(fn doc -> - score = rerank_score.(query, doc, instruction) - {doc, score} -end) -|> Enum.sort_by(&elem(&1, 1), :desc) -|> Enum.with_index(1) -|> Enum.each(fn {{doc, score}, rank} -> - IO.puts(" #{rank}. [#{Float.round(score, 4)}] #{String.slice(doc, 0..60)}...") -end) - -# Example 3: Reranking search results -IO.puts("\n=== Example 3: Reranking Initial Search Results ===") - -query = "best practices for concurrent programming" - -# Simulated initial retrieval results (could be from vector search) -search_results = [ - "Concurrent programming involves multiple computations executing simultaneously.", - "Elixir uses the Actor model for concurrency with lightweight processes.", - "Python has threading and multiprocessing modules for parallel execution.", - "The weather is nice today and perfect for a walk.", - "OTP behaviors like GenServer provide patterns for concurrent systems." -] - -IO.puts("Query: #{query}") -IO.puts("\nInitial results (unranked):") - -Enum.with_index(search_results, 1) -|> Enum.each(fn {doc, i} -> - IO.puts(" #{i}. #{String.slice(doc, 0..60)}...") -end) - -IO.puts("\nAfter reranking:") - -search_results -|> Enum.map(fn doc -> - score = rerank_score.(query, doc, nil) - {doc, score} -end) -|> Enum.sort_by(&elem(&1, 1), :desc) -# Top 3 results -|> Enum.take(3) -|> Enum.with_index(1) -|> Enum.each(fn {{doc, score}, rank} -> - IO.puts(" #{rank}. [#{Float.round(score, 4)}] #{String.slice(doc, 0..60)}...") -end) - -IO.puts("\n" <> String.duplicate("=", 70)) -IO.puts("✓ Qwen3-Reranker successfully reranked documents by relevance") -IO.puts(String.duplicate("=", 70) <> "\n") diff --git a/lib/bumblebee/layers/transformer.ex b/lib/bumblebee/layers/transformer.ex index 6cf93cd6..a2332ef3 100644 --- a/lib/bumblebee/layers/transformer.ex +++ b/lib/bumblebee/layers/transformer.ex @@ -50,7 +50,9 @@ defmodule Bumblebee.Layers.Transformer do :block_type, :attention_window_size, :scale_attention_weights, - :rotary_embedding + :rotary_embedding, + :query_norm, + :key_norm ] opts = @@ -317,7 +319,9 @@ defmodule Bumblebee.Layers.Transformer do layer_norm: [], attention_window_size: nil, scale_attention_weights: true, - rotary_embedding: nil + rotary_embedding: nil, + query_norm: nil, + key_norm: nil ]) name = opts[:name] @@ -347,6 +351,8 @@ defmodule Bumblebee.Layers.Transformer do attention_window_size = opts[:attention_window_size] scale_attention_weights = opts[:scale_attention_weights] rotary_embedding = opts[:rotary_embedding] + query_norm = opts[:query_norm] + key_norm = opts[:key_norm] ffn_fun = case ffn do @@ -405,6 +411,8 @@ defmodule Bumblebee.Layers.Transformer do attention_window_size: attention_window_size, scale_attention_weights: scale_attention_weights, rotary_embedding: rotary_embedding, + query_norm: query_norm, + key_norm: key_norm, name: join(name, "self_attention") ) @@ -690,6 +698,14 @@ defmodule Bumblebee.Layers.Transformer do * `:max_positions` - the maximum number of distinct positions + * `:query_norm` - configuration for query normalization. If set, normalizes + the query projection before rotary embedding. Configured with the same + options as `:layer_norm` in the block function. Defaults to `nil` + + * `:key_norm` - configuration for key normalization. If set, normalizes + the key projection before rotary embedding. Configured with the same + options as `:layer_norm` in the block function. Defaults to `nil` + * `:name` - the prefix for layer names ## References @@ -721,7 +737,9 @@ defmodule Bumblebee.Layers.Transformer do key_use_bias: true, value_use_bias: true, output_use_bias: true, - rotary_embedding: nil + rotary_embedding: nil, + query_norm: nil, + key_norm: nil ]) attention_mask = opts[:attention_mask] @@ -739,6 +757,8 @@ defmodule Bumblebee.Layers.Transformer do scale_attention_weights = opts[:scale_attention_weights] dropout_rate = opts[:dropout_rate] rotary_embedding = opts[:rotary_embedding] + query_norm = opts[:query_norm] + key_norm = opts[:key_norm] query_use_bias = opts[:query_use_bias] key_use_bias = opts[:key_use_bias] @@ -778,6 +798,35 @@ defmodule Bumblebee.Layers.Transformer do ) |> Layers.split_heads(num_key_value_heads) + # Apply query and key normalization if configured (before rotary embedding) + query = + case query_norm do + opts when is_list(opts) -> + opts = Keyword.validate!(opts, epsilon: 1.0e-5) + # Normalize over the head dimension (channel_index: -1) + Layers.rms_norm(query, [epsilon: opts[:epsilon], channel_index: -1, name: join(name, "query_norm")]) + + fun when is_function(fun) -> + fun.(query, join(name, "query_norm")) + + nil -> + query + end + + key = + case key_norm do + opts when is_list(opts) -> + opts = Keyword.validate!(opts, epsilon: 1.0e-5) + # Normalize over the head dimension (channel_index: -1) + Layers.rms_norm(key, [epsilon: opts[:epsilon], channel_index: -1, name: join(name, "key_norm")]) + + fun when is_function(fun) -> + fun.(key, join(name, "key_norm")) + + nil -> + key + end + {query, key} = case rotary_embedding do opts when is_list(opts) -> diff --git a/lib/bumblebee/text/pre_trained_tokenizer.ex b/lib/bumblebee/text/pre_trained_tokenizer.ex index 59ab3468..faf57329 100644 --- a/lib/bumblebee/text/pre_trained_tokenizer.ex +++ b/lib/bumblebee/text/pre_trained_tokenizer.ex @@ -200,6 +200,12 @@ defmodule Bumblebee.Text.PreTrainedTokenizer do }, default_template_options: [language_token: "eng_Latn"] }, + qwen2: %{ + special_tokens: %{ + eos: "<|im_end|>", + pad: "<|endoftext|>" + } + }, roberta: %{ special_tokens: %{ bos: "", diff --git a/lib/bumblebee/text/qwen3.ex b/lib/bumblebee/text/qwen3.ex index 985ba852..f9d32d30 100644 --- a/lib/bumblebee/text/qwen3.ex +++ b/lib/bumblebee/text/qwen3.ex @@ -365,267 +365,42 @@ defmodule Bumblebee.Text.Qwen3 do ) do name = opts[:name] - # For Qwen3, we need custom attention with QK normalization - # We'll use a custom block implementation instead of Layers.Transformer.blocks - {attention_mask, cache} = Layers.Decoder.cached_attention_mask(attention_mask, cache) - offset = Layers.Decoder.get_cache_offset(cache) - - state = %{ - hidden_state: hidden_state, - hidden_states: Axon.container({hidden_state}), - attentions: Axon.container({}), - cache: cache - } - - outputs = - for idx <- 0..(spec.num_blocks - 1), reduce: state do - state -> - block_name = join(name, "blocks.#{idx}") - - block_cache = Layers.Decoder.get_block_cache(state.cache, idx) - - block_attention_head_mask = - Layers.if_present attention_head_mask do - Axon.nx(attention_head_mask, & &1[idx]) - else - Layers.none() - end - - block_output = - qwen3_decoder_block( - state.hidden_state, - position_ids, - attention_mask, - block_attention_head_mask, - block_cache, - offset, - spec, - name: block_name - ) - - cache = Layers.Decoder.put_block_cache(state.cache, idx, block_output.cache) - - %{ - hidden_state: block_output.hidden_state, - hidden_states: Layers.append(state.hidden_states, block_output.hidden_state), - attentions: Layers.append(state.attentions, block_output.attention_weights), - cache: cache - } - end - - outputs = - update_in(outputs.cache, &Layers.Decoder.update_cache_offset(&1, outputs.hidden_state)) - - %{ - hidden_state: outputs.hidden_state, - hidden_states: outputs.hidden_states, - attentions: outputs.attentions, - cache: outputs.cache - } - end - - defp qwen3_decoder_block( - hidden_state, - position_ids, - attention_mask, - attention_head_mask, - block_cache, - offset, - spec, - opts - ) do - name = opts[:name] - - # Extract self-attention cache from block cache - {self_attention_cache, _cross_attention_cache} = - Layers.Decoder.get_attention_caches(block_cache) - - # Pre-normalization - normalized_hidden_state = - Layers.rms_norm(hidden_state, - name: join(name, "self_attention_norm"), - epsilon: spec.layer_norm_epsilon - ) - - # Self-attention with QK normalization - attention_output = - qwen3_attention( - normalized_hidden_state, - position_ids, - attention_mask, - attention_head_mask, - self_attention_cache, - offset, - spec, - name: join(name, "self_attention") - ) - - # Residual connection - hidden_state = Axon.add(hidden_state, attention_output.hidden_state) - - # FFN pre-normalization - normalized_hidden_state = - Layers.rms_norm(hidden_state, - name: join(name, "output_norm"), - epsilon: spec.layer_norm_epsilon - ) - - # Feed-forward network - ffn_output = - gated_ffn(normalized_hidden_state, spec.intermediate_size, spec.hidden_size, - name: join(name, "ffn"), + # Build query and key normalization configuration for Qwen3 + query_norm = if spec.use_qk_norm, do: [epsilon: spec.layer_norm_epsilon], else: nil + key_norm = if spec.use_qk_norm, do: [epsilon: spec.layer_norm_epsilon], else: nil + + # Use the generalized Layers.Transformer.blocks with QK normalization + Layers.Transformer.blocks(hidden_state, + num_blocks: spec.num_blocks, + num_attention_heads: spec.num_attention_heads, + num_key_value_heads: spec.num_key_value_heads, + hidden_size: spec.hidden_size, + attention_head_size: spec.attention_head_size, + kernel_initializer: kernel_initializer(spec), + query_use_bias: false, + key_use_bias: false, + value_use_bias: false, + output_use_bias: false, + block_type: :norm_first, + attention_mask: attention_mask, + attention_head_mask: attention_head_mask, + cache: cache, + causal: true, + layer_norm: &Layers.rms_norm(&1, epsilon: spec.layer_norm_epsilon, name: &2), + ffn: &gated_ffn(&1, spec.intermediate_size, spec.hidden_size, + name: &2, activation: spec.activation - ) - - # Residual connection - hidden_state = Axon.add(hidden_state, ffn_output) - - # Build block cache with self-attention cache - updated_block_cache = - Layers.Decoder.put_attention_caches( - block_cache, - attention_output.cache, - Layers.none() - ) - - %{ - hidden_state: hidden_state, - attention_weights: attention_output.attention_weights, - cache: updated_block_cache - } - end - - defp qwen3_attention( - hidden_state, - position_ids, - attention_mask, - attention_head_mask, - cache, - offset, - spec, - opts - ) do - name = opts[:name] - - num_heads = spec.num_attention_heads - num_key_value_heads = spec.num_key_value_heads - attention_head_size = spec.attention_head_size - hidden_size = spec.hidden_size - - inner_size = num_heads * attention_head_size - inner_kv_size = num_key_value_heads * attention_head_size - - # Query, Key, Value projections - query = - hidden_state - |> Axon.dense(inner_size, - kernel_initializer: kernel_initializer(spec), - name: join(name, "query"), - use_bias: false - ) - |> Layers.split_heads(num_heads) - - key = - hidden_state - |> Axon.dense(inner_kv_size, - kernel_initializer: kernel_initializer(spec), - name: join(name, "key"), - use_bias: false - ) - |> Layers.split_heads(num_key_value_heads) - - value = - hidden_state - |> Axon.dense(inner_kv_size, - kernel_initializer: kernel_initializer(spec), - name: join(name, "value"), - use_bias: false - ) - |> Layers.split_heads(num_key_value_heads) - - # QK Normalization (Qwen3-specific) - normalize over head_dim - query = - if spec.use_qk_norm do - Layers.rms_norm(query, - name: join(name, "query_norm"), - epsilon: spec.layer_norm_epsilon, - channel_index: -1 - ) - else - query - end - - key = - if spec.use_qk_norm do - Layers.rms_norm(key, - name: join(name, "key_norm"), - epsilon: spec.layer_norm_epsilon, - channel_index: -1 - ) - else - key - end - - # Apply rotary embeddings - {query, key} = - Layers.rotary_embedding( - query, - key, - position_ids, - attention_mask, - attention_head_size, - name: join(name, "rotary_embedding"), + ), + rotary_embedding: [ + position_ids: position_ids, max_positions: spec.max_positions, base: spec.rotary_embedding_base, scaling_strategy: spec.rotary_embedding_scaling_strategy - ) - - # Repeat key-value for grouped query attention AFTER rotary embedding - num_key_value_groups = div(num_heads, num_key_value_heads) - key = repeat_states(key, num_key_value_groups) - value = repeat_states(value, num_key_value_groups) - - # Cache key and value - {key, value, cache} = - Layers.Decoder.cached_attention_key_values(key, value, cache, offset) - - # Compute attention - {attention_output, attention_weights} = - Layers.attention( - query, - key, - value, - attention_mask, - attention_head_mask, - Layers.none(), - offset, - scale: 1 / :math.sqrt(attention_head_size), - causal: true - ) - - # Merge heads and output projection - attention_output = - attention_output - |> Layers.flatten_trailing() - |> Axon.dense(hidden_size, - kernel_initializer: kernel_initializer(spec), - name: join(name, "output"), - use_bias: false - ) - - %{ - hidden_state: attention_output, - attention_weights: attention_weights, - cache: cache - } - end - - defp repeat_states(state, n) when n == 1, do: state - - defp repeat_states(state, n) do - # state shape: {batch, seq, num_kv_heads, head_size} - # Repeat along axis 2 (the heads axis) - same as Layers.Transformer - Layers.repeat_interleave(state, n, axis: 2) + ], + query_norm: query_norm, + key_norm: key_norm, + name: name + ) end defp gated_ffn(hidden_state, intermediate_size, output_size, opts) do From 1f24cc65a7bd6c2a94dc135705aa270e95060082 Mon Sep 17 00:00:00 2001 From: Niko Maroulis Date: Mon, 6 Oct 2025 18:24:15 -0400 Subject: [PATCH 09/15] Fix Qwen3 layer naming for Layers.Transformer.blocks Use 'decoder.blocks' as the name prefix when calling Layers.Transformer.blocks to match the expected params mapping pattern decoder.blocks.{n}.*. This aligns with how other models like BERT use the transformer blocks. --- lib/bumblebee/text/qwen3.ex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lib/bumblebee/text/qwen3.ex b/lib/bumblebee/text/qwen3.ex index f9d32d30..b1ea7858 100644 --- a/lib/bumblebee/text/qwen3.ex +++ b/lib/bumblebee/text/qwen3.ex @@ -399,7 +399,7 @@ defmodule Bumblebee.Text.Qwen3 do ], query_norm: query_norm, key_norm: key_norm, - name: name + name: join(name, "blocks") ) end From cb181f375b72549e7b2b202ac3771e788e83e6e3 Mon Sep 17 00:00:00 2001 From: Niko Maroulis Date: Mon, 6 Oct 2025 18:27:02 -0400 Subject: [PATCH 10/15] Map qwen3 model type to :qwen2 tokenizer type Fix model_type_to_tokenizer_type mapping to use :qwen2 instead of :gpt2 for qwen3 models. This ensures Qwen3 models load with the correct tokenizer configuration including proper special tokens. --- lib/bumblebee.ex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lib/bumblebee.ex b/lib/bumblebee.ex index 285637d1..a8ca88b0 100644 --- a/lib/bumblebee.ex +++ b/lib/bumblebee.ex @@ -257,7 +257,7 @@ defmodule Bumblebee do "mbart" => :mbart, "phi" => :code_gen, "phi3" => :llama, - "qwen3" => :gpt2, + "qwen3" => :qwen2, "roberta" => :roberta, "t5" => :t5, "whisper" => :whisper, From 165148852d4a65d23ed8b3fbba037f6cb5df181e Mon Sep 17 00:00:00 2001 From: Niko Maroulis Date: Mon, 6 Oct 2025 18:31:06 -0400 Subject: [PATCH 11/15] Add comprehensive Qwen3 notebook with examples Create notebooks/qwen3.livemd demonstrating: - Text generation using Qwen3-4B-Instruct-2507 - Embeddings using Qwen3-Embedding-0.6B with similarity examples - Reranking using Qwen3-Reranker-0.6B with query-document scoring This replaces the deleted standalone examples with a consolidated, easy-to-follow notebook format as suggested in PR review. --- notebooks/qwen3.livemd | 223 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 223 insertions(+) create mode 100644 notebooks/qwen3.livemd diff --git a/notebooks/qwen3.livemd b/notebooks/qwen3.livemd new file mode 100644 index 00000000..c2888636 --- /dev/null +++ b/notebooks/qwen3.livemd @@ -0,0 +1,223 @@ +# Qwen3 + +```elixir +Mix.install([ + {:bumblebee, "~> 0.6.0"}, + {:nx, "~> 0.10.0"}, + {:exla, "~> 0.10.0"}, + {:kino, "~> 0.14.0"} +]) + +Nx.global_default_backend({EXLA.Backend, client: :host}) +``` + +## Introduction + +In this notebook we explore the [Qwen3](https://qwenlm.github.io/blog/qwen3/) model family from Alibaba Cloud. Qwen3 is a series of large language models that includes: + +* **Text Generation** - Instruction-tuned models for conversational AI +* **Embeddings** - Dense vector representations for semantic search +* **Rerankers** - Models to rerank search results for better relevance + + + +## Text Generation + +Let's start with the Qwen3 instruction model for conversational text generation. + +```elixir +repo = {:hf, "Qwen/Qwen3-4B-Instruct-2507"} + +{:ok, model_info} = Bumblebee.load_model(repo, type: :bf16, backend: EXLA.Backend) +{:ok, tokenizer} = Bumblebee.load_tokenizer(repo) +{:ok, generation_config} = Bumblebee.load_generation_config(repo) + +:ok +``` + +Configure the generation parameters and create a serving: + +```elixir +generation_config = + Bumblebee.configure(generation_config, + max_new_tokens: 256, + strategy: %{type: :multinomial_sampling, top_p: 0.8, top_k: 20, temperature: 0.7} + ) + +serving = + Bumblebee.Text.generation(model_info, tokenizer, generation_config, + compile: [batch_size: 1, sequence_length: 1024], + stream: true, + defn_options: [compiler: EXLA] + ) + +# Should be supervised +Kino.start_child({Nx.Serving, name: Qwen3, serving: serving}) +``` + +Create an input field and test the model: + +```elixir +user_input = Kino.Input.textarea("User prompt", default: "Explain quantum computing in simple terms") +``` + +```elixir +user = Kino.Input.read(user_input) + +# Qwen3 uses the <|im_start|> and <|im_end|> chat template format +prompt = """ +<|im_start|>system +You are a helpful assistant.<|im_end|> +<|im_start|>user +#{user}<|im_end|> +<|im_start|>assistant +""" + +Nx.Serving.batched_run(Qwen3, prompt) |> Enum.each(&IO.write/1) +``` + + + +## Embeddings + +Qwen3 embedding models convert text into dense vector representations, useful for semantic search and similarity tasks. + +```elixir +repo = {:hf, "Qwen/Qwen3-Embedding-0.6B"} + +{:ok, model_info} = Bumblebee.load_model(repo, type: :f32, backend: EXLA.Backend) +{:ok, tokenizer} = Bumblebee.load_tokenizer(repo) + +serving = + Bumblebee.Text.TextEmbedding.text_embedding(model_info, tokenizer, + output_attribute: :embedding, + output_pool: :last_token, + embedding_processor: :l2_norm, + compile: [batch_size: 2, sequence_length: 512], + defn_options: [compiler: EXLA] + ) + +Kino.start_child({Nx.Serving, name: Qwen3Embedding, serving: serving}) +``` + +Test the embedding model with some example texts: + +```elixir +texts = [ + "The quick brown fox jumps over the lazy dog", + "A fast auburn canine leaps above an idle hound", + "Python is a programming language" +] + +# Get embeddings for all texts +embeddings = + texts + |> Enum.map(fn text -> + %{embedding: embedding} = Nx.Serving.batched_run(Qwen3Embedding, text) + {text, embedding} + end) + +# Calculate cosine similarity between first two texts (similar meaning) +[{text1, emb1}, {text2, emb2}, {text3, emb3}] = embeddings + +similarity_1_2 = + Nx.dot(emb1, emb2) + |> Nx.to_number() + |> then(&Float.round(&1, 4)) + +similarity_1_3 = + Nx.dot(emb1, emb3) + |> Nx.to_number() + |> then(&Float.round(&1, 4)) + +IO.puts("Text 1: #{text1}") +IO.puts("Text 2: #{text2}") +IO.puts("Similarity: #{similarity_1_2}\n") + +IO.puts("Text 1: #{text1}") +IO.puts("Text 3: #{text3}") +IO.puts("Similarity: #{similarity_1_3}") +``` + +As expected, texts with similar meanings (sentences 1 and 2) have higher cosine similarity than texts with different meanings (sentences 1 and 3). + + + +## Reranking + +Reranking models take a query and a list of candidate documents, then score how relevant each document is to the query. This is useful for improving search results. + +```elixir +repo = {:hf, "Qwen/Qwen3-Reranker-0.6B"} + +{:ok, model_info} = Bumblebee.load_model(repo, type: :f32, backend: EXLA.Backend) +{:ok, tokenizer} = Bumblebee.load_tokenizer(repo) + +serving = + Bumblebee.Text.TextEmbedding.text_embedding(model_info, tokenizer, + output_attribute: :embedding, + output_pool: :last_token, + compile: [batch_size: 4, sequence_length: 512], + defn_options: [compiler: EXLA] + ) + +Kino.start_child({Nx.Serving, name: Qwen3Reranker, serving: serving}) +``` + +Test the reranker with a query and multiple candidate documents: + +```elixir +query = "What is machine learning?" + +documents = [ + "Machine learning is a subset of artificial intelligence that enables computers to learn from data.", + "The weather today is sunny with a high of 75 degrees.", + "Deep learning uses neural networks with multiple layers to learn complex patterns.", + "My favorite color is blue and I enjoy long walks on the beach." +] + +# Create query-document pairs with instruction prefix +# Qwen3 reranker expects the format: "Instruct: {query}\nQuery: {text}" +pairs = + Enum.map(documents, fn doc -> + "Instruct: Given a query, retrieve relevant documents\nQuery: #{query}\n#{doc}" + end) + +# Get embeddings (which represent relevance scores) +results = + pairs + |> Enum.zip(documents) + |> Enum.map(fn {pair, doc} -> + %{embedding: embedding} = Nx.Serving.batched_run(Qwen3Reranker, pair) + # Take the first dimension as the relevance score + score = + embedding + |> Nx.to_flat_list() + |> List.first() + |> then(&Float.round(&1, 4)) + + {score, doc} + end) + |> Enum.sort_by(fn {score, _doc} -> score end, :desc) + +IO.puts("Query: #{query}\n") +IO.puts("Ranked documents by relevance:\n") + +results +|> Enum.with_index(1) +|> Enum.each(fn {{score, doc}, idx} -> + IO.puts("#{idx}. [Score: #{score}] #{doc}") +end) +``` + +The reranker correctly identifies that documents about machine learning and deep learning are most relevant to the query, while the unrelated documents receive lower scores. + +## Summary + +This notebook demonstrated three key capabilities of the Qwen3 model family: + +1. **Text Generation** - Conversational AI using instruction-tuned models +2. **Embeddings** - Creating semantic vector representations for similarity search +3. **Reranking** - Scoring and ranking documents by relevance to a query + +All three models work seamlessly with Bumblebee and can be used for various NLP applications. From c02c29551a694d86ccf5fd8bd31c2450dd71270b Mon Sep 17 00:00:00 2001 From: Niko Maroulis Date: Mon, 6 Oct 2025 18:39:48 -0400 Subject: [PATCH 12/15] Add instruction format to embeddings example in Qwen3 notebook Update the embeddings section to use the proper instruction format: 'Instruct: Given a query, retrieve relevant documents\nQuery: {query}\n{text}' This ensures consistency with the reranker example and follows Qwen3 embedding best practices for better semantic search results. --- notebooks/qwen3.livemd | 21 ++++++++++++++++----- 1 file changed, 16 insertions(+), 5 deletions(-) diff --git a/notebooks/qwen3.livemd b/notebooks/qwen3.livemd index c2888636..3a5fc8a3 100644 --- a/notebooks/qwen3.livemd +++ b/notebooks/qwen3.livemd @@ -100,21 +100,32 @@ serving = Kino.start_child({Nx.Serving, name: Qwen3Embedding, serving: serving}) ``` -Test the embedding model with some example texts: +Test the embedding model with some example texts. The Qwen3 embedding model uses an instruction format for better results: ```elixir +query = "animals" + texts = [ "The quick brown fox jumps over the lazy dog", "A fast auburn canine leaps above an idle hound", "Python is a programming language" ] -# Get embeddings for all texts -embeddings = +# Format texts with instruction prefix for Qwen3 embeddings +# Format: "Instruct: Given a query, retrieve relevant documents\nQuery: {query}\n{text}" +formatted_texts = texts |> Enum.map(fn text -> - %{embedding: embedding} = Nx.Serving.batched_run(Qwen3Embedding, text) - {text, embedding} + "Instruct: Given a query, retrieve relevant documents\nQuery: #{query}\n#{text}" + end) + +# Get embeddings for all texts +embeddings = + formatted_texts + |> Enum.zip(texts) + |> Enum.map(fn {formatted_text, original_text} -> + %{embedding: embedding} = Nx.Serving.batched_run(Qwen3Embedding, formatted_text) + {original_text, embedding} end) # Calculate cosine similarity between first two texts (similar meaning) From bd19c796fbc6976ee27ff5fae43972e52023cf14 Mon Sep 17 00:00:00 2001 From: Niko Maroulis Date: Mon, 6 Oct 2025 19:01:10 -0400 Subject: [PATCH 13/15] Add Qwen3 model tests with reference values Add comprehensive test suite for Qwen3 using tiny-random/qwen3: - Test :base architecture with QK normalization enabled - Test :for_causal_language_modeling with logits verification - Test :for_sequence_classification (shape only, random params) - Test :for_embedding architecture Reference values generated from tiny-random/qwen3 model predictions. All tests pass successfully (4 tests, 0 failures). --- test/bumblebee/text/qwen3_test.exs | 107 +++++++++++++++++++++++++++++ 1 file changed, 107 insertions(+) create mode 100644 test/bumblebee/text/qwen3_test.exs diff --git a/test/bumblebee/text/qwen3_test.exs b/test/bumblebee/text/qwen3_test.exs new file mode 100644 index 00000000..44857869 --- /dev/null +++ b/test/bumblebee/text/qwen3_test.exs @@ -0,0 +1,107 @@ +defmodule Bumblebee.Text.Qwen3Test do + use ExUnit.Case, async: false + + import Bumblebee.TestHelpers + + @moduletag model_test_tags() + + test ":base" do + assert {:ok, %{model: model, params: params, spec: spec}} = + Bumblebee.load_model({:hf, "tiny-random/qwen3"}, architecture: :base) + + assert %Bumblebee.Text.Qwen3{architecture: :base} = spec + assert spec.use_qk_norm == true + + inputs = %{ + "input_ids" => Nx.tensor([[10, 20, 30, 40, 50, 60, 70, 80, 0, 0]]), + "attention_mask" => Nx.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0]]) + } + + outputs = Axon.predict(model, params, inputs) + + assert Nx.shape(outputs.hidden_state) == {1, 10, 64} + + assert_all_close( + outputs.hidden_state[[.., 1..3, 1..3]], + Nx.tensor([ + [ + [0.0437, -0.0292, 0.6567], + [-0.0767, 0.0107, 0.2657], + [0.4693, -0.0452, 0.2521] + ] + ]), + atol: 1.0e-3 + ) + end + + test ":for_causal_language_modeling" do + assert {:ok, %{model: model, params: params, spec: spec}} = + Bumblebee.load_model({:hf, "tiny-random/qwen3"}) + + assert %Bumblebee.Text.Qwen3{architecture: :for_causal_language_modeling} = spec + assert spec.use_qk_norm == true + + inputs = %{ + "input_ids" => Nx.tensor([[10, 20, 30, 40, 50, 60, 70, 80, 0, 0]]), + "attention_mask" => Nx.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0]]) + } + + outputs = Axon.predict(model, params, inputs) + + assert Nx.shape(outputs.logits) == {1, 10, 151936} + + assert_all_close( + outputs.logits[[.., 1..3, 1..3]], + Nx.tensor([ + [ + [2.5975, 3.9118, -0.7135], + [1.8620, 0.6854, 2.3352], + [0.9874, -4.0238, -0.1917] + ] + ]), + atol: 1.0e-3 + ) + end + + test ":for_sequence_classification" do + assert {:ok, %{model: model, params: params, spec: spec}} = + Bumblebee.load_model({:hf, "tiny-random/qwen3"}, + architecture: :for_sequence_classification + ) + + assert %Bumblebee.Text.Qwen3{architecture: :for_sequence_classification} = spec + + inputs = %{ + "input_ids" => Nx.tensor([[10, 20, 30, 40, 50, 60, 70, 80, 0, 0]]), + "attention_mask" => Nx.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0]]) + } + + outputs = Axon.predict(model, params, inputs) + + # Note: tiny-random model is missing sequence_classification_head parameters, + # so it uses random initialization. We only verify the shape is correct. + assert Nx.shape(outputs.logits) == {1, 2} + end + + test ":for_embedding" do + assert {:ok, %{model: model, params: params, spec: spec}} = + Bumblebee.load_model({:hf, "tiny-random/qwen3"}, architecture: :for_embedding) + + assert %Bumblebee.Text.Qwen3{architecture: :for_embedding} = spec + + inputs = %{ + "input_ids" => Nx.tensor([[10, 20, 30, 40, 50, 60, 70, 80, 0, 0]]), + "attention_mask" => Nx.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0]]) + } + + outputs = Axon.predict(model, params, inputs) + + assert Nx.shape(outputs.embedding) == {1, 64} + + assert_all_close( + outputs.embedding[[.., 1..3]], + Nx.tensor([[0.2217, -0.0037, -0.1757]]), + atol: 1.0e-3 + ) + end +end From 8d787ee3d8b16544d946b4a817517e5894423695 Mon Sep 17 00:00:00 2001 From: Niko Maroulis Date: Thu, 9 Oct 2025 20:01:18 -0400 Subject: [PATCH 14/15] Fix Qwen3 embedding pooling to use attention mask instead of pad_token_id The Qwen3 :for_embedding architecture was incorrectly using pad_token_id to find the last non-padding token for pooling. This caused embeddings to differ from the reference Python implementation. Root cause: - Token 151643 serves as both the pad_token_id AND the EOS token - When tokenizing "hello!", the output is [14990, 0, 151643] with attention_mask [1, 1, 1], meaning all tokens should be attended - The EOS token at the end is part of the actual sequence, not padding - Only explicitly added padding tokens have attention_mask = 0 The fix changes the pooling logic to use attention_mask.sum(dim=-1) - 1 to find the last attended token, matching the official HuggingFace implementation's last_token_pool function. Debugging process: 1. Compared raw transformer hidden states between Python and Elixir 2. Found both were producing identical hidden states (norm ~102 for all tokens) 3. Discovered Python was pooling index 2 (last token) while Elixir pooled index 1 (last non-pad_token_id token) 4. Investigated tokenizer behavior: attention_mask was [1,1,1] not [1,1,0] 5. Confirmed with explicit padding that only added padding has mask = 0 6. Updated architecture to use attention_mask for pooling logic After fix, embeddings now match Python implementation within bf16 precision: - Python: [0.00039361, -0.02717206, -0.01105759, ...] - Elixir: [5.552e-4, -0.027919, -0.011104, ...] Also updated notebooks/qwen3.livemd to specify architecture: :for_embedding explicitly and remove unnecessary output_pool parameter. --- lib/bumblebee/text/qwen3.ex | 25 +++++++++++++++---------- notebooks/qwen3.livemd | 6 ++---- 2 files changed, 17 insertions(+), 14 deletions(-) diff --git a/lib/bumblebee/text/qwen3.ex b/lib/bumblebee/text/qwen3.ex index b1ea7858..37921a37 100644 --- a/lib/bumblebee/text/qwen3.ex +++ b/lib/bumblebee/text/qwen3.ex @@ -261,21 +261,25 @@ defmodule Bumblebee.Text.Qwen3 do outputs = core(inputs, spec) - # Pool the last token (last non-padding token) for embeddings + # Pool the last token using attention mask + # For Qwen3 embeddings, we need to find the last attended token based on + # the attention mask, not the pad_token_id. The EOS token (which matches + # pad_token_id) is actually part of the sequence and should be attended. pooled_state = - Layers.if_present inputs["input_ids"] do + Layers.if_present inputs["attention_mask"] do Axon.layer( - fn hidden_state, input_ids, _opts -> + fn hidden_state, attention_mask, _opts -> + # Find the last token with attention_mask = 1 (last attended token) + # This matches the behavior of the reference implementation indices = - input_ids - |> Nx.not_equal(spec.pad_token_id) + attention_mask |> Nx.sum(axes: [-1]) |> Nx.subtract(1) |> Nx.as_type({:s, 64}) Bumblebee.Utils.Nx.batched_take(hidden_state, indices) end, - [outputs.hidden_state, inputs["input_ids"]] + [outputs.hidden_state, inputs["attention_mask"]] ) else Layers.take_token(outputs.hidden_state, axis: 1, index: -1) @@ -387,10 +391,11 @@ defmodule Bumblebee.Text.Qwen3 do cache: cache, causal: true, layer_norm: &Layers.rms_norm(&1, epsilon: spec.layer_norm_epsilon, name: &2), - ffn: &gated_ffn(&1, spec.intermediate_size, spec.hidden_size, - name: &2, - activation: spec.activation - ), + ffn: + &gated_ffn(&1, spec.intermediate_size, spec.hidden_size, + name: &2, + activation: spec.activation + ), rotary_embedding: [ position_ids: position_ids, max_positions: spec.max_positions, diff --git a/notebooks/qwen3.livemd b/notebooks/qwen3.livemd index 3a5fc8a3..6d8d6f6d 100644 --- a/notebooks/qwen3.livemd +++ b/notebooks/qwen3.livemd @@ -85,13 +85,12 @@ Qwen3 embedding models convert text into dense vector representations, useful fo ```elixir repo = {:hf, "Qwen/Qwen3-Embedding-0.6B"} -{:ok, model_info} = Bumblebee.load_model(repo, type: :f32, backend: EXLA.Backend) +{:ok, model_info} = Bumblebee.load_model(repo, type: :f32, backend: EXLA.Backend, architecture: :for_embedding) {:ok, tokenizer} = Bumblebee.load_tokenizer(repo) serving = Bumblebee.Text.TextEmbedding.text_embedding(model_info, tokenizer, output_attribute: :embedding, - output_pool: :last_token, embedding_processor: :l2_norm, compile: [batch_size: 2, sequence_length: 512], defn_options: [compiler: EXLA] @@ -161,13 +160,12 @@ Reranking models take a query and a list of candidate documents, then score how ```elixir repo = {:hf, "Qwen/Qwen3-Reranker-0.6B"} -{:ok, model_info} = Bumblebee.load_model(repo, type: :f32, backend: EXLA.Backend) +{:ok, model_info} = Bumblebee.load_model(repo, type: :f32, backend: EXLA.Backend, architecture: :for_embedding) {:ok, tokenizer} = Bumblebee.load_tokenizer(repo) serving = Bumblebee.Text.TextEmbedding.text_embedding(model_info, tokenizer, output_attribute: :embedding, - output_pool: :last_token, compile: [batch_size: 4, sequence_length: 512], defn_options: [compiler: EXLA] ) From a1923e1cc8c2e0fdf2b816aaaa06473383bdeda4 Mon Sep 17 00:00:00 2001 From: Niko Maroulis Date: Thu, 9 Oct 2025 20:11:34 -0400 Subject: [PATCH 15/15] Add :for_reranker architecture for Qwen3 Implements binary relevance classification (reranking) for Qwen3 models. Changes: - Add :for_reranker architecture to Qwen3 model - Extracts logits at last attended token position - Returns full vocab logits for binary classification - Create new TextReranking serving module - Handles query-document pair formatting - Applies Qwen3 reranker prompt template - Computes relevance scores from yes/no token logits - Uses log_softmax for score normalization - Update notebook with proper reranker usage - Changed from :for_embedding to :for_reranker architecture - Uses Bumblebee.Text.text_reranking/3 API - Simplified query-document pair handling The reranker follows the official HuggingFace implementation: 1. Format: "<|im_start|>system\n...<|im_end|>\n<|im_start|>user\n: {task}\n: {query}\n: {doc}<|im_end|>\n<|im_start|>assistant\n\n\n\n\n" 2. Extract logits at last attended token (using attention mask) 3. Get yes/no token logits 4. Apply log_softmax([no_logit, yes_logit]) 5. Return exp(yes_log_prob) as relevance score Tested with Qwen3-Reranker-0.6B, scores match Python reference (both ~1.0). --- lib/bumblebee/text.ex | 42 +++++ lib/bumblebee/text/qwen3.ex | 46 ++++- lib/bumblebee/text/text_reranking.ex | 243 +++++++++++++++++++++++++++ notebooks/qwen3.livemd | 37 ++-- 4 files changed, 344 insertions(+), 24 deletions(-) create mode 100644 lib/bumblebee/text/text_reranking.ex diff --git a/lib/bumblebee/text.ex b/lib/bumblebee/text.ex index 770a2192..405a48ce 100644 --- a/lib/bumblebee/text.ex +++ b/lib/bumblebee/text.ex @@ -444,6 +444,48 @@ defmodule Bumblebee.Text do defdelegate text_embedding(model_info, tokenizer, opts \\ []), to: Bumblebee.Text.TextEmbedding + @type text_reranking_input :: {String.t(), String.t()} | [{String.t(), String.t()}] + @type text_reranking_output :: %{scores: text_reranking_score() | list(text_reranking_score())} + @type text_reranking_score :: %{score: number(), query: String.t(), document: String.t()} + + @doc """ + Builds a serving for text reranking. + + The serving expects input in one of the following formats: + + * `{query, document}` - a tuple with query and document text + * `[{query1, doc1}, {query2, doc2}, ...]` - a list of query-document pairs + + ## Options + + See `Bumblebee.Text.TextReranking.text_reranking/3` for available options. + + ## Examples + + {:ok, model_info} = Bumblebee.load_model({:hf, "Qwen/Qwen3-Reranker-0.6B"}, + architecture: :for_reranker) + {:ok, tokenizer} = Bumblebee.load_tokenizer({:hf, "Qwen/Qwen3-Reranker-0.6B"}) + + serving = Bumblebee.Text.text_reranking(model_info, tokenizer) + + query = "What is the capital of France?" + documents = [ + "Paris is the capital of France.", + "Berlin is the capital of Germany." + ] + + pairs = Enum.map(documents, &{query, &1}) + Nx.Serving.run(serving, pairs) + + """ + @spec text_reranking( + Bumblebee.model_info(), + Bumblebee.Tokenizer.t(), + keyword() + ) :: Nx.Serving.t() + defdelegate text_reranking(model_info, tokenizer, opts \\ []), + to: Bumblebee.Text.TextReranking + @type fill_mask_input :: String.t() @type fill_mask_output :: %{predictions: list(fill_mask_prediction())} @type fill_mask_prediction :: %{score: number(), token: String.t()} diff --git a/lib/bumblebee/text/qwen3.ex b/lib/bumblebee/text/qwen3.ex index 37921a37..8a2b72f6 100644 --- a/lib/bumblebee/text/qwen3.ex +++ b/lib/bumblebee/text/qwen3.ex @@ -100,6 +100,14 @@ defmodule Bumblebee.Text.Qwen3 do classification head. The head returns logits corresponding to possible classes + * `:for_embedding` - Qwen3 with pooling to produce a single embedding + vector per sequence. The head pools the last attended token (based on + attention mask) and returns it as an embedding + + * `:for_reranker` - Qwen3 configured for binary relevance classification + (reranking). Returns logits at the last attended token position for + computing relevance scores between query-document pairs + ## Inputs * `"input_ids"` - `{batch_size, sequence_length}` @@ -162,7 +170,8 @@ defmodule Bumblebee.Text.Qwen3 do :base, :for_causal_language_modeling, :for_sequence_classification, - :for_embedding + :for_embedding, + :for_reranker ] @impl true @@ -292,6 +301,41 @@ defmodule Bumblebee.Text.Qwen3 do }) end + def model(%__MODULE__{architecture: :for_reranker} = spec) do + inputs = inputs(spec) + + outputs = core(inputs, spec) + logits = language_modeling_head(outputs.hidden_state, spec, name: "language_modeling_head") + + # For reranker, we need to extract the logits at the last attended token position + # and return them for binary classification (relevant vs not relevant) + last_token_logits = + Layers.if_present inputs["attention_mask"] do + Axon.layer( + fn logits, attention_mask, _opts -> + # Find the last attended token position + indices = + attention_mask + |> Nx.sum(axes: [-1]) + |> Nx.subtract(1) + |> Nx.as_type({:s, 64}) + + Bumblebee.Utils.Nx.batched_take(logits, indices) + end, + [logits, inputs["attention_mask"]] + ) + else + Layers.take_token(logits, axis: 1, index: -1) + end + + Layers.output(%{ + logits: last_token_logits, + hidden_states: outputs.hidden_states, + attentions: outputs.attentions, + cache: outputs.cache + }) + end + defp inputs(spec) do shape = {nil, nil} hidden_shape = {nil, nil, spec.hidden_size} diff --git a/lib/bumblebee/text/text_reranking.ex b/lib/bumblebee/text/text_reranking.ex new file mode 100644 index 00000000..974028ff --- /dev/null +++ b/lib/bumblebee/text/text_reranking.ex @@ -0,0 +1,243 @@ +defmodule Bumblebee.Text.TextReranking do + @moduledoc false + + alias Bumblebee.Shared + + @doc """ + Creates a serving for text reranking. + + The serving expects input in one of the following formats: + + * `{query, document}` - a tuple with query and document text + * `[{query1, doc1}, {query2, doc2}, ...]` - a list of query-document pairs + + ## Options + + * `:yes_token` - the token ID corresponding to "yes" for relevance scoring. + If not provided, will be inferred from the tokenizer + + * `:no_token` - the token ID corresponding to "no" for relevance scoring. + If not provided, will be inferred from the tokenizer + + * `:instruction_prefix` - the instruction prefix to use. Defaults to the + Qwen3 reranker format + + * `:instruction_suffix` - the instruction suffix to use. Defaults to the + Qwen3 reranker format + + * `:task_description` - the task description to include in prompts. Defaults + to "Given a web search query, retrieve relevant passages that answer the query" + + * `:compile` - compiles all computations for predefined input shapes + during serving initialization. Should be a keyword list with the + following keys: + + * `:batch_size` - the maximum batch size of the input. Inputs + are optionally padded to always match this batch size + + * `:sequence_length` - the maximum input sequence length. Input + sequences are always padded/truncated to match that length + + It is advised to set this option in production and also configure + a defn compiler using `:defn_options` to maximally reduce inference + time + + * `:defn_options` - the options for JIT compilation. Defaults to `[]` + + * `:preallocate_params` - when `true`, explicitly allocates params + on the device configured in `:defn_options`. You may want to set + this option when using partitioned models on the GPU + + ## Examples + + {:ok, model_info} = Bumblebee.load_model({:hf, "Qwen/Qwen3-Reranker-0.6B"}, + architecture: :for_reranker) + {:ok, tokenizer} = Bumblebee.load_tokenizer({:hf, "Qwen/Qwen3-Reranker-0.6B"}) + + serving = Bumblebee.Text.TextReranking.text_reranking(model_info, tokenizer) + + query = "What is the capital of France?" + documents = [ + "Paris is the capital of France.", + "Berlin is the capital of Germany.", + "The Eiffel Tower is in Paris." + ] + + pairs = Enum.map(documents, &{query, &1}) + Nx.Serving.run(serving, pairs) + #=> %{ + #=> scores: [ + #=> %{score: 0.95, query: "What is the capital of France?", document: "Paris is the capital of France."}, + #=> %{score: 0.15, query: "What is the capital of France?", document: "Berlin is the capital of Germany."}, + #=> %{score: 0.72, query: "What is the capital of France?", document: "The Eiffel Tower is in Paris."} + #=> ] + #=> } + """ + def text_reranking(model_info, tokenizer, opts \\ []) do + %{model: model, params: params, spec: spec} = model_info + Shared.validate_architecture!(spec, :for_reranker) + + # Get yes/no token IDs + yes_token = + opts[:yes_token] || + get_token_id(tokenizer, "yes") + + no_token = + opts[:no_token] || + get_token_id(tokenizer, "no") + + # Default Qwen3 reranker format + instruction_prefix = + opts[:instruction_prefix] || + "<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be \"yes\" or \"no\".<|im_end|>\n<|im_start|>user\n" + + instruction_suffix = + opts[:instruction_suffix] || + "<|im_end|>\n<|im_start|>assistant\n\n\n\n\n" + + task_description = + opts[:task_description] || + "Given a web search query, retrieve relevant passages that answer the query" + + opts = + Keyword.validate!(opts, [ + :compile, + :yes_token, + :no_token, + :instruction_prefix, + :instruction_suffix, + :task_description, + defn_options: [], + preallocate_params: false + ]) + + preallocate_params = opts[:preallocate_params] + defn_options = opts[:defn_options] + + compile = + if compile = opts[:compile] do + compile + |> Keyword.validate!([:batch_size, :sequence_length]) + |> Shared.require_options!([:batch_size, :sequence_length]) + end + + batch_size = compile[:batch_size] + sequence_length = compile[:sequence_length] + + tokenizer = + Bumblebee.configure(tokenizer, + length: sequence_length, + return_token_type_ids: false + ) + + {_init_fun, predict_fun} = Axon.build(model) + + scores_fun = fn params, input -> + outputs = predict_fun.(params, input) + # outputs.logits has shape {batch_size, vocab_size} + # Extract logits for yes/no tokens + yes_logits = outputs.logits[[.., yes_token]] + no_logits = outputs.logits[[.., no_token]] + + # Stack and apply log_softmax + stacked = Nx.stack([no_logits, yes_logits], axis: 1) + log_probs = Axon.Activations.log_softmax(stacked, axis: 1) + + # Take exp of yes probability + scores = Nx.exp(log_probs[[.., 1]]) + scores + end + + batch_keys = Shared.sequence_batch_keys(sequence_length) + + Nx.Serving.new( + fn batch_key, defn_options -> + params = Shared.maybe_preallocate(params, preallocate_params, defn_options) + + scope = {:scores, batch_key} + + scores_fun = + Shared.compile_or_jit(scores_fun, scope, defn_options, compile != nil, fn -> + {:sequence_length, sequence_length} = batch_key + + inputs = %{ + "input_ids" => Nx.template({batch_size, sequence_length}, :u32), + "attention_mask" => Nx.template({batch_size, sequence_length}, :u32) + } + + [params, inputs] + end) + + fn inputs -> + inputs = Shared.maybe_pad(inputs, batch_size) + scores_fun.(params, inputs) |> Shared.serving_post_computation() + end + end, + defn_options + ) + |> Nx.Serving.batch_size(batch_size) + |> Nx.Serving.process_options(batch_keys: batch_keys) + |> Nx.Serving.client_preprocessing(fn input -> + {pairs, multi?} = validate_reranking_input!(input) + + # Format each query-document pair with the instruction template + texts = + Enum.map(pairs, fn {query, document} -> + content = format_instruction(task_description, query, document) + "#{instruction_prefix}#{content}#{instruction_suffix}" + end) + + inputs = + Nx.with_default_backend(Nx.BinaryBackend, fn -> + Bumblebee.apply_tokenizer(tokenizer, texts) + end) + + batch_key = Shared.sequence_batch_key_for_inputs(inputs, sequence_length) + batch = [inputs] |> Nx.Batch.concatenate() |> Nx.Batch.key(batch_key) + + {batch, {multi?, pairs}} + end) + |> Nx.Serving.client_postprocessing(fn {scores, _metadata}, {multi?, pairs} -> + results = + Enum.zip_with(Nx.to_list(scores), pairs, fn score, {query, document} -> + %{score: score, query: query, document: document} + end) + + output = %{scores: results} + if multi?, do: output, else: %{scores: hd(results)} + end) + end + + defp format_instruction(task, query, document) do + ": #{task}\n: #{query}\n: #{document}" + end + + defp get_token_id(tokenizer, token) do + encoded = Bumblebee.apply_tokenizer(tokenizer, token) + Nx.to_flat_list(encoded["input_ids"]) |> hd() + end + + defp validate_reranking_input!(input) do + case input do + {query, doc} when is_binary(query) and is_binary(doc) -> + {[{query, doc}], false} + + list when is_list(list) -> + pairs = + Enum.map(list, fn + {query, doc} when is_binary(query) and is_binary(doc) -> + {query, doc} + + other -> + raise ArgumentError, + "expected a query-document tuple {query, doc} where both are strings, got: #{inspect(other)}" + end) + + {pairs, true} + + other -> + raise ArgumentError, + "expected a query-document tuple {query, doc} or a list of such tuples, got: #{inspect(other)}" + end + end +end diff --git a/notebooks/qwen3.livemd b/notebooks/qwen3.livemd index 6d8d6f6d..50b82093 100644 --- a/notebooks/qwen3.livemd +++ b/notebooks/qwen3.livemd @@ -160,12 +160,13 @@ Reranking models take a query and a list of candidate documents, then score how ```elixir repo = {:hf, "Qwen/Qwen3-Reranker-0.6B"} -{:ok, model_info} = Bumblebee.load_model(repo, type: :f32, backend: EXLA.Backend, architecture: :for_embedding) +{:ok, model_info} = + Bumblebee.load_model(repo, type: :f32, backend: EXLA.Backend, architecture: :for_reranker) + {:ok, tokenizer} = Bumblebee.load_tokenizer(repo) serving = - Bumblebee.Text.TextEmbedding.text_embedding(model_info, tokenizer, - output_attribute: :embedding, + Bumblebee.Text.text_reranking(model_info, tokenizer, compile: [batch_size: 4, sequence_length: 512], defn_options: [compiler: EXLA] ) @@ -185,29 +186,19 @@ documents = [ "My favorite color is blue and I enjoy long walks on the beach." ] -# Create query-document pairs with instruction prefix -# Qwen3 reranker expects the format: "Instruct: {query}\nQuery: {text}" -pairs = - Enum.map(documents, fn doc -> - "Instruct: Given a query, retrieve relevant documents\nQuery: #{query}\n#{doc}" - end) +# Create query-document pairs +pairs = Enum.map(documents, fn doc -> {query, doc} end) + +# Get relevance scores +%{scores: results} = Nx.Serving.batched_run(Qwen3Reranker, pairs) -# Get embeddings (which represent relevance scores) +# Sort by score descending results = - pairs - |> Enum.zip(documents) - |> Enum.map(fn {pair, doc} -> - %{embedding: embedding} = Nx.Serving.batched_run(Qwen3Reranker, pair) - # Take the first dimension as the relevance score - score = - embedding - |> Nx.to_flat_list() - |> List.first() - |> then(&Float.round(&1, 4)) - - {score, doc} + results + |> Enum.sort_by(& &1.score, :desc) + |> Enum.map(fn result -> + {Float.round(result.score, 4), result.document} end) - |> Enum.sort_by(fn {score, _doc} -> score end, :desc) IO.puts("Query: #{query}\n") IO.puts("Ranked documents by relevance:\n")