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Model: ik-ram28/BioMistral-CPT-SFT-7B
Source: Original Platform

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README.md

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+---
+library_name: transformers
+tags:
+- medical
+license: apache-2.0
+language:
+- fr
+- en
+base_model:
+- ik-ram28/BioMistral-CPT-7B
+- BioMistral/BioMistral-7B
+---
+
+## Model Description
+
+BioMistral-CPT-SFT-7B is a French medical language model based on BioMistral-7B, adapted for French medical domain applications through a combined approach of Continual Pre-Training (CPT) followed by Supervised Fine-Tuning (SFT). 
+
+## Model Details
+
+- **Model Type**: Causal Language Model
+- **Base Model**: BioMistral-7B 
+- **Language**: French (adapted from English medical model)
+- **Domain**: Medical/Healthcare
+- **Parameters**: 7 billion
+- **License**: Apache 2.0
+- **Paper**: [Adaptation des connaissances médicales pour les grands modèles de langue : Stratégies et analyse comparative](https://github.com/ikram28/medllm-strategies)
+
+## Training Details
+
+### Continual Pre-Training (CPT)
+- **Dataset**: NACHOS corpus (opeN crAwled frenCh Healthcare cOrpuS)
+  - **Size**: 7.4 GB of French medical texts
+  - **Word Count**: Over 1 billion words
+  - **Sources**: 24 French medical websites
+- **Training Duration**: 2.8 epochs
+- **Hardware**: 32 NVIDIA H100 80GB GPUs
+- **Training Time**: 11 hours
+- **Optimizer**: AdamW
+- **Learning Rate**: 2e-5
+- **Weight Decay**: 0.01
+- **Batch Size**: 16 with gradient accumulation of 2
+
+### Supervised Fine-Tuning (SFT)
+- **Dataset**: 30K French medical question-answer pairs
+  - 10K native French medical questions
+  - 10K translated medical questions from English resources
+  - 10K generated questions from French medical texts
+- **Method**: DoRA (Weight-Decomposed Low-Rank Adaptation)
+- **Training Duration**: 10 epochs
+- **Hardware**: 1 NVIDIA H100 80GB GPU
+- **Training Time**: 42 hours
+- **Rank**: 16
+- **Alpha**: 16
+- **Learning Rate**: 2e-5
+- **Batch Size**: 4
+
+
+
+
+## Computational Impact
+
+- **Total Training Time**: 53 hours (11h CPT + 42h SFT)
+- **Hardware**: 32 GPU H100 + 1 GPU H100
+- **Carbon Emissions**: 10.11 kgCO2e (9.04 + 1.07)
+
+
+
+## Ethical Considerations
+
+- **Medical Accuracy**: This model is for research and educational purposes only. Performance limitations make it unsuitable for critical medical applications
+- **Bias**: May contain biases from both English and French medical literature
+
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+
+```
+
+## Contact
+
+For questions about this model, please contact: ikram.belmadani@lis-lab.fr

config.json

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+  "architectures": [
+    "MistralForCausalLM"
+  ],
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+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
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+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.2",
+  "use_cache": false,
+  "vocab_size": 32000
+}

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+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.45.2"
+}

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+}

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": null,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "chat_template": "{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content'] %}\n    {%- set loop_messages = messages[1:] %}\n{%- else %}\n    {%- set loop_messages = messages %}\n{%- endif %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}\n        {{- raise_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }}\n    {%- endif %}\n    {%- if message['role'] == 'user' %}\n        {%- if loop.first and system_message is defined %}\n            {{- ' [INST] ' + system_message + '\\n\\n' + message['content'] + ' [/INST]' }}\n        {%- else %}\n            {{- ' [INST] ' + message['content'] + ' [/INST]' }}\n        {%- endif %}\n    {%- elif message['role'] == 'assistant' %}\n        {{- ' ' + message['content'] + eos_token}}\n    {%- else %}\n        {{- raise_exception('Only user and assistant roles are supported, with the exception of an initial optional system message!') }}\n    {%- endif %}\n{%- endfor %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": false,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": null,
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}