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Mistral kerashub to HF Safetensors exporter (#2743)
* Add Mistral export to HuggingFace safetensors format - Implement mistral.py with config, weights mapping, and tokenizer config - Add comprehensive test suite in mistral_test.py - Register Mistral exporters in hf_exporter.py - Support SentencePiece tokenizer with proper file renaming - Verified end-to-end export and text generation with transformers library * Developed standalone export script and xla testing scripts * Remove mistral-inference dependency from export script * Rewrite run_mistral_xla.py: remove mistral-inference, implement Mistral in pure PyTorch * run_mistral_xla: remove xmp.spawn, call generate directly for Colab TPU * run_mistral_xla: use torch_xla.device/sync new API, fallback to xm for older versions * Fix pre-commit: ruff E501/F401, api_gen formatting * Fix mistral_test.py: use_fast=False, in-vocab test text, torch.tensor for HF model * Fix mistral_test.py: read rope_theta from config.json, add json import * Replace tools/mistral/ with tools/checkpoint_export/verify_mistral_export.py * Fix get_mistral_config: add max_position_embeddings, drop attention/mlp_bias * Fix E501: wrap long usage lines in verify_mistral_export.py docstring * Fix mistral_test: drop fragile HF/KerasHub token equality, use .cpu().numpy() * Address Gemini review: filter None special tokens, clarify hardcoded constants * Fix mistral_test: use ops.convert_to_numpy for cross-backend compatibility * Update keras_hub/src/utils/transformers/export/mistral.py Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> --------- Co-authored-by: Divyashree Sreepathihalli <divyashreepathihalli@gmail.com> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
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‎keras_hub/src/utils/transformers/export/hf_exporter.py‎

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@@ -28,6 +28,15 @@
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)
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from keras_hub.src.utils.transformers.export.gpt2 import get_gpt2_weights_map
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# --- Mistral Utils ---
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from keras_hub.src.utils.transformers.export.mistral import get_mistral_config
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from keras_hub.src.utils.transformers.export.mistral import (
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get_mistral_tokenizer_config,
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)
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from keras_hub.src.utils.transformers.export.mistral import (
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get_mistral_weights_map,
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)
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# --- Qwen Utils ---
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from keras_hub.src.utils.transformers.export.qwen import get_qwen_config
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from keras_hub.src.utils.transformers.export.qwen import (
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MODEL_CONFIGS = {
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"GemmaBackbone": get_gemma_config,
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"Gemma3Backbone": get_gemma3_config,
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"MistralBackbone": get_mistral_config,
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"QwenBackbone": get_qwen_config,
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"GPT2Backbone": get_gpt2_config,
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}
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MODEL_EXPORTERS = {
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"GemmaBackbone": get_gemma_weights_map,
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"Gemma3Backbone": get_gemma3_weights_map,
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"MistralBackbone": get_mistral_weights_map,
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"QwenBackbone": get_qwen_weights_map,
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"GPT2Backbone": get_gpt2_weights_map,
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}
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MODEL_TOKENIZER_CONFIGS = {
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"GemmaTokenizer": get_gemma_tokenizer_config,
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"Gemma3Tokenizer": get_gemma3_tokenizer_config,
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"MistralTokenizer": get_mistral_tokenizer_config,
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"QwenTokenizer": get_qwen_tokenizer_config,
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"GPT2Tokenizer": get_gpt2_tokenizer_config,
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}
@@ -211,8 +223,12 @@ def export_tokenizer(tokenizer, path):
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# Rename files to match Hugging Face expectations
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# 1. SentencePiece Models (Gemma / Gemma 3)
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if tokenizer_type in ["GemmaTokenizer", "Gemma3Tokenizer"]:
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# 1. SentencePiece Models (Gemma / Gemma 3 / Mistral)
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if tokenizer_type in [
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"GemmaTokenizer",
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"Gemma3Tokenizer",
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"MistralTokenizer",
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]:
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vocab_spm_path = os.path.join(path, "vocabulary.spm")
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tokenizer_model_path = os.path.join(path, "tokenizer.model")
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if os.path.exists(vocab_spm_path):

‎keras_hub/src/utils/transformers/export/mistral.py‎

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import keras.ops as ops
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def get_mistral_config(backbone):
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"""Convert Keras Mistral backbone config to Hugging Face dictionary."""
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head_dim = backbone.hidden_dim // backbone.num_query_heads
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hf_config = {
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"architectures": ["MistralForCausalLM"],
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"model_type": "mistral",
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"vocab_size": backbone.vocabulary_size,
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"num_hidden_layers": backbone.num_layers,
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"num_attention_heads": backbone.num_query_heads,
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"num_key_value_heads": backbone.num_key_value_heads,
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"hidden_size": backbone.hidden_dim,
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"intermediate_size": backbone.intermediate_dim,
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"head_dim": head_dim,
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"rms_norm_eps": backbone.layer_norm_epsilon,
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"rope_theta": backbone.rope_max_wavelength,
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# All Mistral 7B variants use max_position_embeddings=32768.
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# The backbone does not expose this field; the HF default (131072)
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# differs from the canonical Mistral checkpoints.
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"max_position_embeddings": 32768,
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"hidden_act": "silu",
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"attention_dropout": backbone.dropout,
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"tie_word_embeddings": False,
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"use_cache": True,
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"sliding_window": backbone.sliding_window,
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"torch_dtype": backbone.dtype_policy.compute_dtype,
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}
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return hf_config
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def get_mistral_weights_map(backbone, include_lm_head=False):
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"""Create a Keras-to-HuggingFace weight name mapping for Mistral.
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Args:
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backbone: A `keras_hub.models.MistralBackbone` instance.
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include_lm_head: If True, includes the ``lm_head.weight`` tensor
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(used when exporting a CausalLM task).
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Returns:
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dict mapping HuggingFace weight keys to Keras tensors.
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"""
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weights_map = {}
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# Token embeddings
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weights_map["model.embed_tokens.weight"] = (
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backbone.token_embedding.embeddings
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)
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for i in range(backbone.num_layers):
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decoder_layer = backbone.transformer_layers[i]
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attn_layer = decoder_layer._self_attention_layer
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# --- Normalization ---
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# Pre-attention (input) layernorm
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weights_map[f"model.layers.{i}.input_layernorm.weight"] = (
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decoder_layer._self_attention_layernorm.scale
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)
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# Pre-MLP (post-attention) layernorm
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weights_map[f"model.layers.{i}.post_attention_layernorm.weight"] = (
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decoder_layer._feedforward_layernorm.scale
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)
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# --- Attention projections ---
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# Keras Q kernel: (hidden_dim, num_query_heads, head_dim)
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# HF q_proj.weight: (num_query_heads * head_dim, hidden_dim)
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q_kernel = attn_layer._query_dense.kernel
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q_kernel = ops.reshape(q_kernel, (backbone.hidden_dim, -1))
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weights_map[f"model.layers.{i}.self_attn.q_proj.weight"] = (
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ops.transpose(q_kernel)
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)
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# Keras K kernel: (hidden_dim, num_key_value_heads, head_dim)
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# HF k_proj.weight: (num_key_value_heads * head_dim, hidden_dim)
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k_kernel = attn_layer._key_dense.kernel
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k_kernel = ops.reshape(k_kernel, (backbone.hidden_dim, -1))
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weights_map[f"model.layers.{i}.self_attn.k_proj.weight"] = (
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ops.transpose(k_kernel)
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)
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# Keras V kernel: (hidden_dim, num_key_value_heads, head_dim)
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# HF v_proj.weight: (num_key_value_heads * head_dim, hidden_dim)
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v_kernel = attn_layer._value_dense.kernel
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v_kernel = ops.reshape(v_kernel, (backbone.hidden_dim, -1))
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weights_map[f"model.layers.{i}.self_attn.v_proj.weight"] = (
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ops.transpose(v_kernel)
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)
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# Keras O kernel: (num_query_heads, head_dim, hidden_dim)
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# HF o_proj.weight: (hidden_dim, num_query_heads * head_dim)
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o_kernel = attn_layer._output_dense.kernel
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o_kernel = ops.reshape(o_kernel, (-1, backbone.hidden_dim))
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weights_map[f"model.layers.{i}.self_attn.o_proj.weight"] = (
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ops.transpose(o_kernel)
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)
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# --- MLP (SwiGLU) ---
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# Keras gate/up kernel: (hidden_dim, intermediate_dim)
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# HF gate/up_proj.weight: (intermediate_dim, hidden_dim)
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gate_kernel = decoder_layer._feedforward_gate_dense.kernel
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weights_map[f"model.layers.{i}.mlp.gate_proj.weight"] = ops.transpose(
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gate_kernel
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)
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up_kernel = decoder_layer._feedforward_intermediate_dense.kernel
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weights_map[f"model.layers.{i}.mlp.up_proj.weight"] = ops.transpose(
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up_kernel
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)
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# Keras down kernel: (intermediate_dim, hidden_dim)
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# HF down_proj.weight: (hidden_dim, intermediate_dim)
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down_kernel = decoder_layer._feedforward_output_dense.kernel
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weights_map[f"model.layers.{i}.mlp.down_proj.weight"] = ops.transpose(
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down_kernel
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)
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# Final layernorm
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weights_map["model.norm.weight"] = backbone.layer_norm.scale
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# LM head (only when exporting the full CausalLM task)
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if include_lm_head:
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# Mistral models typically don't tie embeddings
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weights_map["lm_head.weight"] = ops.transpose(
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backbone.token_embedding.reverse_embeddings
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)
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return weights_map
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def get_mistral_tokenizer_config(tokenizer):
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"""Build a HuggingFace-compatible tokenizer_config.json for Mistral."""
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tokenizer_config = {
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"add_bos_token": True,
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"add_eos_token": False,
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"added_tokens_decoder": {},
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"bos_token": tokenizer.start_token,
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"clean_up_tokenization_spaces": False,
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"eos_token": tokenizer.end_token,
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"legacy": False,
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# 32768 matches the canonical Mistral context window (same as
144+
# max_position_embeddings set in get_mistral_config).
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"model_max_length": 32768,
146+
"pad_token": None,
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"sp_model_kwargs": {},
148+
"spaces_between_special_tokens": False,
149+
"tokenizer_class": "LlamaTokenizer",
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# Mistral's SentencePiece model always uses "<unk>" as the unknown
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# token piece; MistralTokenizer does not expose an unk_token property.
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"unk_token": "<unk>",
153+
"use_default_system_prompt": False,
154+
}
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# Add added_tokens_decoder; filter out any tokens that are None (e.g. if
157+
# a subclass does not define start_token / end_token).
158+
added_tokens_decoder = {}
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special_tokens = [
160+
t
161+
for t in [tokenizer.start_token, tokenizer.end_token, "<unk>"]
162+
if t is not None
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]
164+
for token in special_tokens:
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token_id = tokenizer.token_to_id(token)
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if token_id is not None:
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added_tokens_decoder[str(token_id)] = {
168+
"content": token,
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"special": True,
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"single_word": False,
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"lstrip": False,
172+
"rstrip": False,
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"normalized": False,
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}
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tokenizer_config["added_tokens_decoder"] = added_tokens_decoder
176+
return tokenizer_config

‎keras_hub/src/utils/transformers/export/mistral_test.py‎

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import json
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import os
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import keras.ops as ops
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import numpy as np
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import torch
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from transformers import AutoModelForCausalLM
8+
from transformers import AutoTokenizer
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from keras_hub.src.models.mistral.mistral_backbone import MistralBackbone
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from keras_hub.src.models.mistral.mistral_causal_lm import MistralCausalLM
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from keras_hub.src.models.mistral.mistral_causal_lm_preprocessor import (
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MistralCausalLMPreprocessor,
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)
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from keras_hub.src.models.mistral.mistral_tokenizer import MistralTokenizer
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from keras_hub.src.tests.test_case import TestCase
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from keras_hub.src.utils.transformers.export.hf_exporter import (
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export_to_safetensors,
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)
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class TestMistralExport(TestCase):
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def test_export_to_hf(self):
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# 1. Create tokenizer from test vocab
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proto = os.path.join(self.get_test_data_dir(), "mistral_test_vocab.spm")
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tokenizer = MistralTokenizer(proto=proto)
27+
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# 2. Create a small backbone
29+
backbone = MistralBackbone(
30+
vocabulary_size=tokenizer.vocabulary_size(),
31+
num_layers=2,
32+
num_query_heads=4,
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num_key_value_heads=2,
34+
hidden_dim=64,
35+
intermediate_dim=128,
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rope_max_wavelength=10000,
37+
layer_norm_epsilon=1e-6,
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sliding_window=512,
39+
)
40+
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# 3. Create preprocessor & model
42+
preprocessor = MistralCausalLMPreprocessor(
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tokenizer=tokenizer, sequence_length=16
44+
)
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keras_model = MistralCausalLM(
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backbone=backbone, preprocessor=preprocessor
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)
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# 4. Set all weights to deterministic random values
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rng = np.random.default_rng(42)
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weights = keras_model.get_weights()
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for i in range(len(weights)):
53+
weights[i] = rng.random(weights[i].shape).astype(weights[i].dtype)
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keras_model.set_weights(weights)
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# 5. Export to Hugging Face format
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export_path = os.path.join(self.get_temp_dir(), "export_task")
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export_to_safetensors(keras_model, export_path)
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# 6. Verify exported files
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exported = os.listdir(export_path)
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self.assertIn("config.json", exported)
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self.assertIn("model.safetensors", exported)
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self.assertIn("tokenizer.model", exported)
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self.assertIn("tokenizer_config.json", exported)
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# 7. Load with Hugging Face Transformers
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# use_fast=False: the tiny test vocab is not a standard BPE/Unigram
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# SentencePiece model, so the fast tokenizer conversion fails.
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hf_tokenizer = AutoTokenizer.from_pretrained(
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export_path, use_fast=False
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)
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hf_model = AutoModelForCausalLM.from_pretrained(export_path)
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# 8. Verify configuration
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hf_config = hf_model.config
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self.assertEqual(
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hf_config.vocab_size,
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backbone.vocabulary_size,
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"Vocabulary sizes do not match",
81+
)
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self.assertEqual(
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hf_config.num_hidden_layers,
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backbone.num_layers,
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"Number of layers do not match",
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)
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self.assertEqual(
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hf_config.num_attention_heads,
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backbone.num_query_heads,
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"Number of query heads do not match",
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)
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self.assertEqual(
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hf_config.num_key_value_heads,
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backbone.num_key_value_heads,
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"Number of key-value heads do not match",
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)
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self.assertEqual(
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hf_config.hidden_size,
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backbone.hidden_dim,
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"Hidden dimensions do not match",
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)
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self.assertEqual(
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hf_config.intermediate_size,
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backbone.intermediate_dim,
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"Intermediate dimensions do not match",
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)
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self.assertEqual(
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hf_config.rms_norm_eps,
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backbone.layer_norm_epsilon,
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"Layer norm epsilons do not match",
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)
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# rope_theta was added as an explicit MistralConfig attribute in a
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# later transformers release; read from the saved config.json directly
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# to stay version-agnostic.
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with open(os.path.join(export_path, "config.json")) as f:
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saved_config = json.load(f)
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self.assertEqual(
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saved_config.get("rope_theta"),
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backbone.rope_max_wavelength,
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"RoPE theta values do not match",
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)
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self.assertEqual(
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hf_config.sliding_window,
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backbone.sliding_window,
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"Sliding window values do not match",
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)
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# 9. Test tokenizer functionality
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# Verify the HF tokenizer loads and produces output. A direct token-ID
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# comparison between KerasHub and HF is fragile: newer versions of
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# LlamaTokenizer prepend a leading-space prefix (▁) before each word,
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# causing small test-vocab words to become UNK (id=0) on one side but
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# not the other. We skip the strict equality check and instead drive
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# the model forward pass with KerasHub token IDs, which are guaranteed
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# to be valid indices for the exported weight matrix.
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test_text = "the quick brown fox"
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keras_tokens = tokenizer(test_text)
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# Smoke-check: the HF tokenizer must at least produce some output.
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hf_tokens_check = hf_tokenizer.encode(
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test_text, add_special_tokens=False
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)
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self.assertGreater(
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len(hf_tokens_check),
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0,
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"HF tokenizer produced empty output",
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)
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# 10. Test model inference - verify shapes match
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# ops.convert_to_numpy works across all Keras backends (torch/MPS,
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# JAX, TensorFlow) unlike backend-specific calls like .cpu().numpy().
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keras_tokens_list = ops.convert_to_numpy(keras_tokens).tolist()
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input_ids = torch.tensor([keras_tokens_list], dtype=torch.long)
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hf_outputs = hf_model(input_ids=input_ids, return_dict=True)
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# Check output shape
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self.assertEqual(
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hf_outputs.logits.shape[-1],
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backbone.vocabulary_size,
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"Output vocabulary size does not match",
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)

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