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* first commit * uncomment * other tests adaptations * Remove unused variable in test_setup_chat_format * Remove unused import statement * style * Add Bart model * Update BCOTrainerTester class in test_bco_trainer.py * Update model IDs and tokenizers in test files * Add new models and processors * Update model IDs in test files * Fix formatting issue in test_dataset_formatting.py * Refactor dataset formatting in test_dataset_formatting.py * Fix dataset sequence length in SFTTrainerTester * Remove tokenizer * Remove print statement * Add reward_model_path and sft_model_path to PPO trainer * Fix tokenizer padding issue * Add chat template for testing purposes in PaliGemma model * Update PaliGemma model and chat template * Increase learning rate to speed up test * Update model names in run_dpo.sh and run_sft.sh scripts * Update model and dataset names * Fix formatting issue in test_dataset_formatting.py * Fix formatting issue in test_dataset_formatting.py * Remove unused chat template * Update model generation script * additional models * Update model references in test files * Remove unused imports in test_online_dpo_trainer.py * Add is_llm_blender_available import and update reward_tokenizer * Refactor test_online_dpo_trainer.py: Move skipped test case decorator * remove models without chat templates * Update model names in scripts and tests * Update model_id in test_modeling_value_head.py * Update model versions in test files * Fix formatting issue in test_dataset_formatting.py * Update embedding model ID in BCOTrainerTester * Update test_online_dpo_trainer.py with reward model changes * Update expected formatted text in test_dataset_formatting.py * Add reward_tokenizer to TestOnlineDPOTrainer * fix tests * Add SIMPLE_CHAT_TEMPLATE to T5 tokenizer * Fix dummy_text format in test_rloo_trainer.py * Skip outdated test for chatML data collator * Add new vision language models * Commented out unused model IDs in test_vdpo_trainer * Update model and vision configurations in generate_tiny_models.py and test_dpo_trainer.py * Update model and tokenizer references * Don't push if it already exists * Add comment explaining test skip * Fix model_exists function call and add new models * Update LlavaForConditionalGeneration model and processor * `qgallouedec` -> `trl-internal-testing`
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| Original file line number | Diff line number | Diff line change |
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| # Copyright 2024 The HuggingFace Inc. team. All rights reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
|
||
| # This script generates tiny models used in the TRL library for unit tests. It pushes them to the Hub under the | ||
| # `trl-internal-testing` organization. | ||
| # This script is meant to be run when adding new tiny model to the TRL library. | ||
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| from huggingface_hub import HfApi, ModelCard | ||
| from transformers import ( | ||
| AutoProcessor, | ||
| AutoTokenizer, | ||
| BartConfig, | ||
| BartModel, | ||
| BloomConfig, | ||
| BloomForCausalLM, | ||
| CLIPVisionConfig, | ||
| CohereConfig, | ||
| CohereForCausalLM, | ||
| DbrxConfig, | ||
| DbrxForCausalLM, | ||
| FalconMambaConfig, | ||
| FalconMambaForCausalLM, | ||
| Gemma2Config, | ||
| Gemma2ForCausalLM, | ||
| GemmaConfig, | ||
| GemmaForCausalLM, | ||
| GPT2Config, | ||
| GPT2LMHeadModel, | ||
| GPTNeoXConfig, | ||
| GPTNeoXForCausalLM, | ||
| Idefics2Config, | ||
| Idefics2ForConditionalGeneration, | ||
| LlamaConfig, | ||
| LlamaForCausalLM, | ||
| LlavaConfig, | ||
| LlavaForConditionalGeneration, | ||
| LlavaNextConfig, | ||
| LlavaNextForConditionalGeneration, | ||
| MistralConfig, | ||
| MistralForCausalLM, | ||
| OPTConfig, | ||
| OPTForCausalLM, | ||
| PaliGemmaConfig, | ||
| PaliGemmaForConditionalGeneration, | ||
| Phi3Config, | ||
| Phi3ForCausalLM, | ||
| Qwen2Config, | ||
| Qwen2ForCausalLM, | ||
| SiglipVisionConfig, | ||
| T5Config, | ||
| T5ForConditionalGeneration, | ||
| ) | ||
| from transformers.models.idefics2.configuration_idefics2 import Idefics2VisionConfig | ||
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| ORGANIZATION = "trl-internal-testing" | ||
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| MODEL_CARD = """ | ||
| --- | ||
| library_name: transformers | ||
| tags: [trl] | ||
| --- | ||
| # Tiny {model_class_name} | ||
| This is a minimal model built for unit tests in the [TRL](https://github.com/huggingface/trl) library. | ||
| """ | ||
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| api = HfApi() | ||
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| def push_to_hub(model, tokenizer, suffix=None): | ||
| model_class_name = model.__class__.__name__ | ||
| content = MODEL_CARD.format(model_class_name=model_class_name) | ||
| model_card = ModelCard(content) | ||
| repo_id = f"{ORGANIZATION}/tiny-{model_class_name}" | ||
| if suffix is not None: | ||
| repo_id += f"-{suffix}" | ||
|
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| if api.repo_exists(repo_id): | ||
| print(f"Model {repo_id} already exists, skipping") | ||
| else: | ||
| model.push_to_hub(repo_id) | ||
| tokenizer.push_to_hub(repo_id) | ||
| model_card.push_to_hub(repo_id) | ||
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| # Decoder models | ||
| for model_id, config_class, model_class, suffix in [ | ||
| ("bigscience/bloomz-560m", BloomConfig, BloomForCausalLM, None), | ||
| ("CohereForAI/aya-expanse-8b", CohereConfig, CohereForCausalLM, None), | ||
| ("databricks/dbrx-instruct", DbrxConfig, DbrxForCausalLM, None), | ||
| ("tiiuae/falcon-7b-instruct", FalconMambaConfig, FalconMambaForCausalLM, None), | ||
| ("google/gemma-2-2b-it", Gemma2Config, Gemma2ForCausalLM, None), | ||
| ("google/gemma-7b-it", GemmaConfig, GemmaForCausalLM, None), | ||
| ("openai-community/gpt2", GPT2Config, GPT2LMHeadModel, None), | ||
| ("EleutherAI/pythia-14m", GPTNeoXConfig, GPTNeoXForCausalLM, None), | ||
| ("meta-llama/Meta-Llama-3-8B-Instruct", LlamaConfig, LlamaForCausalLM, "3"), | ||
| ("meta-llama/Llama-3.1-8B-Instruct", LlamaConfig, LlamaForCausalLM, "3.1"), | ||
| ("meta-llama/Llama-3.2-1B-Instruct", LlamaConfig, LlamaForCausalLM, "3.2"), | ||
| ("mistralai/Mistral-7B-Instruct-v0.1", MistralConfig, MistralForCausalLM, "0.1"), | ||
| ("mistralai/Mistral-7B-Instruct-v0.2", MistralConfig, MistralForCausalLM, "0.2"), | ||
| ("facebook/opt-1.3b", OPTConfig, OPTForCausalLM, None), | ||
| ("microsoft/Phi-3.5-mini-instruct", Phi3Config, Phi3ForCausalLM, None), | ||
| ("Qwen/Qwen2.5-32B-Instruct", Qwen2Config, Qwen2ForCausalLM, "2.5"), | ||
| ("Qwen/Qwen2.5-Coder-0.5B", Qwen2Config, Qwen2ForCausalLM, "2.5-Coder"), | ||
| ]: | ||
| tokenizer = AutoTokenizer.from_pretrained(model_id) | ||
| config = config_class( | ||
| vocab_size=tokenizer.vocab_size + len(tokenizer.added_tokens_encoder.keys()), | ||
| hidden_size=8, | ||
| num_attention_heads=4, | ||
| num_key_value_heads=2, | ||
| num_hidden_layers=2, | ||
| intermediate_size=32, | ||
| ) | ||
| model = model_class(config) | ||
| push_to_hub(model, tokenizer, suffix) | ||
|
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||
|
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| # Encoder-decoder models | ||
| for model_id, config_class, model_class, suffix in [ | ||
| ("google/flan-t5-small", T5Config, T5ForConditionalGeneration, None), | ||
| ("facebook/bart-base", BartConfig, BartModel, None), | ||
| ]: | ||
| tokenizer = AutoTokenizer.from_pretrained(model_id) | ||
| config = config_class( | ||
| vocab_size=tokenizer.vocab_size + len(tokenizer.added_tokens_encoder.keys()), | ||
| d_model=16, | ||
| encoder_layers=2, | ||
| decoder_layers=2, | ||
| d_kv=2, | ||
| d_ff=64, | ||
| num_layers=6, | ||
| num_heads=8, | ||
| decoder_start_token_id=0, | ||
| is_encoder_decoder=True, | ||
| ) | ||
| model = model_class(config) | ||
| push_to_hub(model, tokenizer, suffix) | ||
|
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| # Vision Language Models | ||
| # fmt: off | ||
| for model_id, config_class, text_config_class, vision_config_class, model_class in [ | ||
| ("HuggingFaceM4/idefics2-8b", Idefics2Config, MistralConfig, Idefics2VisionConfig, Idefics2ForConditionalGeneration), | ||
| ("llava-hf/llava-1.5-7b-hf", LlavaConfig, LlamaConfig, CLIPVisionConfig, LlavaForConditionalGeneration), | ||
| ("llava-hf/llava-v1.6-mistral-7b-hf", LlavaNextConfig, MistralConfig, CLIPVisionConfig, LlavaNextForConditionalGeneration), | ||
| ("google/paligemma-3b-pt-224", PaliGemmaConfig, GemmaConfig, SiglipVisionConfig, PaliGemmaForConditionalGeneration), | ||
| ]: | ||
| # fmt: on | ||
| processor = AutoProcessor.from_pretrained(model_id) | ||
| kwargs = {} | ||
| if config_class == PaliGemmaConfig: | ||
| kwargs["projection_dim"] = 8 | ||
| vision_kwargs = {} | ||
| if vision_config_class in [CLIPVisionConfig, SiglipVisionConfig]: | ||
| vision_kwargs["projection_dim"] = 8 | ||
| if vision_config_class == CLIPVisionConfig: | ||
| vision_kwargs["image_size"] = 336 | ||
| vision_kwargs["patch_size"] = 14 | ||
| config = config_class( | ||
| text_config=text_config_class( | ||
| vocab_size=processor.tokenizer.vocab_size + len(processor.tokenizer.added_tokens_encoder), | ||
| hidden_size=8, | ||
| num_attention_heads=4, | ||
| num_key_value_heads=2, | ||
| num_hidden_layers=2, | ||
| intermediate_size=32, | ||
| ), | ||
| vision_config=vision_config_class( | ||
| hidden_size=8, | ||
| num_attention_heads=4, | ||
| num_hidden_layers=2, | ||
| intermediate_size=32, | ||
| **vision_kwargs, | ||
| ), | ||
| **kwargs, | ||
| ) | ||
| model = model_class(config) | ||
| push_to_hub(model, processor) |
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