Initial upload: MedGemma 4B-IT French Medical Assistant fine-tuned with LoRA
Browse files- .gitattributes +1 -0
- README.md +182 -0
- added_tokens.json +3 -0
- chat_template.jinja +47 -0
- config.json +62 -0
- generation_config.json +11 -0
- pytorch_model-00001-of-00005.bin +3 -0
- pytorch_model-00002-of-00005.bin +3 -0
- pytorch_model-00003-of-00005.bin +3 -0
- pytorch_model-00004-of-00005.bin +3 -0
- pytorch_model-00005-of-00005.bin +3 -0
- pytorch_model.bin.index.json +891 -0
- special_tokens_map.json +33 -0
- tokenizer.json +3 -0
- tokenizer.model +3 -0
- tokenizer_config.json +8 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: apache-2.0
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base_model: google/medgemma-4b-it
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tags:
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- medical
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- french
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- healthcare
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- text-generation
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- fine-tuned
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- peft
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- lora
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language:
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- fr
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pipeline_tag: text-generation
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widget:
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- example_title: "Question médicale générale"
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text: "Comment traiter une migraine ?"
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- example_title: "Symptômes"
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text: "Quels sont les symptômes de l'hypertension ?"
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- example_title: "Prévention"
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text: "Comment prévenir les infections urinaires ?"
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model-index:
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- name: medgemma-4b-it-french-medical-assistant
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results: []
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---
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# MedGemma 4B-IT Assistant Médical Français
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## Description
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Ce modèle est une version fine-tunée de **google/medgemma-4b-it** spécialement optimisée pour fournir des **réponses médicales courtes et précises en français**.
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Le modèle a été entraîné pour donner des réponses concises (2-3 phrases maximum) tout en maintenant la précision médicale, idéal pour un usage d'assistant médical rapide et efficace.
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## 🎯 Caractéristiques
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- **Modèle de base**: google/medgemma-4b-it
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- **Langue**: Français 🇫🇷
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- **Spécialisation**: Réponses médicales courtes et précises
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- **Technique**: LoRA Fine-tuning
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- **Type**: Text Generation (text-to-text)
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- **Paramètres**: ~4B (avec adaptateurs LoRA)
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## 🚀 Utilisation
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### Installation
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```bash
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pip install transformers torch peft accelerate bitsandbytes
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```
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### Code d'utilisation
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
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from peft import PeftModel
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# Configuration
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model_id = "Sadou/medgemma-4b-it-french-medical-assistant"
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# Chargement du modèle base avec quantisation
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base_model = AutoModelForCausalLM.from_pretrained(
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"google/medgemma-4b-it",
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quantization_config=BitsAndBytesConfig(load_in_4bit=True),
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device_map="auto",
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torch_dtype=torch.bfloat16,
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)
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# Chargement des adaptateurs LoRA
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model = PeftModel.from_pretrained(base_model, model_id)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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def generate_medical_response(question, max_length=150):
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prompt = f"""<start_of_turn>user
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{question}<end_of_turn>
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<start_of_turn>model
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"""
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inputs = tokenizer(prompt, return_tensors="pt").to(device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=max_length,
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do_sample=True,
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temperature=0.7,
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pad_token_id=tokenizer.eos_token_id,
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eos_token_id=tokenizer.eos_token_id,
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)
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response = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
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return response.strip()
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# Exemple d'utilisation
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question = "Comment traiter une migraine ?"
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response = generate_medical_response(question)
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print(f"Question: {question}")
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print(f"Réponse: {response}")
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```
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## 📝 Exemples
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### Exemple 1: Traitement
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**Input:** "Comment traiter une migraine ?"
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**Output:** "Prenez un antalgique (paracétamol 1g ou ibuprofène 600mg) dès les premiers signes. Reposez-vous dans un endroit sombre et silencieux avec une compresse froide sur le front. Si migraines fréquentes (>4/mois), consultez pour un traitement préventif."
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### Exemple 2: Diagnostic
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**Input:** "Comment différencier une pneumonie bactérienne d'une pneumonie virale ?"
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**Output:** "Les pneumonies bactériennes causent souvent une fièvre élevée (>38.5°C) et des expectorations purulentes. Les pneumonies virales présentent plutôt une toux sèche et une fièvre modérée. Une prise de sang (CRP, leucocytes) et une radiographie thoracique aident au diagnostic."
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### Exemple 3: Prévention
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**Input:** "Comment prévenir les infections urinaires ?"
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**Output:** "Buvez 1.5-2L d'eau par jour, urinez après les rapports sexuels et ne vous retenez pas. Essuyez-vous d'avant en arrière, évitez les savons parfumés pour la toilette intime. Portez des sous-vêtements en coton et évitez les vêtements trop serrés."
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## 🎓 Fine-tuning Details
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- **Dataset**: 20 exemples de réponses médicales optimisées en français
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- **Méthode**: LoRA (Low-Rank Adaptation)
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- **Paramètres LoRA**: r=16, alpha=32, dropout=0.1
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- **Modules ciblés**: q_proj, v_proj, k_proj, o_proj
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- **Épochs**: 3
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- **Learning rate**: 2e-4
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## ⚠️ Avertissements et Limitations
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- **Usage médical**: Ce modèle est destiné à l'information générale uniquement
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- **Pas un remplacement**: Ne remplace pas une consultation médicale professionnelle
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- **Urgences**: En cas d'urgence médicale, contactez immédiatement les services d'urgence
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- **Responsabilité**: L'utilisateur est responsable de l'utilisation des informations fournies
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## 🏥 Cas d'usage recommandés
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- ✅ Information médicale générale
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- ✅ Première approche diagnostic
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- ✅ Conseils de prévention
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- ✅ Éducation sanitaire
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- ✅ Assistant pour professionnels de santé
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## 🚫 Cas d'usage déconseillés
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- ❌ Diagnostic médical définitif
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- ❌ Prescription de médicaments
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- ❌ Urgences médicales
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- ❌ Remplacement d'un médecin
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## 📊 Performance
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Le modèle a été optimisé pour:
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- **Concision**: Réponses limitées à 2-3 phrases
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- **Précision**: Informations médicales factuelles
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- **Clarté**: Langage accessible au grand public
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- **Rapidité**: Génération rapide de réponses
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## 👨💻 Auteur
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**Sadou BARRY** - Spécialiste en IA médicale et modèles de langage
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Profil Hugging Face: [Sadou](https://huggingface.co/Sadou)
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Autres modèles:
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- [medgemma-4b-it-sft-lora-crc100k](https://huggingface.co/Sadou/medgemma-4b-it-sft-lora-crc100k)
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- [medalpaca_medical_meadow_medqa](https://huggingface.co/Sadou/medalpaca_medical_meadow_medqa)
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- [DeepSeek-Qwen2.5-3B-WebInstruct-CFT-50K](https://huggingface.co/Sadou/DeepSeek-Qwen2.5-3B-WebInstruct-CFT-50K)
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## 📄 License
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Ce modèle est distribué sous licence Apache 2.0. Voir le fichier LICENSE pour plus de détails.
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## 🙏 Remerciements
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- Google pour le modèle MedGemma de base
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- Hugging Face pour l'infrastructure
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- La communauté open-source pour les outils utilisés
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---
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*Modèle créé avec ❤️ pour améliorer l'accès à l'information médicale en français*
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added_tokens.json
ADDED
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{
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"<image_soft_token>": 262144
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}
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chat_template.jinja
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{{ bos_token }}
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{%- if messages[0]['role'] == 'system' -%}
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{%- if messages[0]['content'] is string -%}
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{%- set first_user_prefix = messages[0]['content'] + '
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' -%}
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{%- else -%}
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{%- set first_user_prefix = messages[0]['content'][0]['text'] + '
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' -%}
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{%- endif -%}
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{%- set loop_messages = messages[1:] -%}
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{%- else -%}
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{%- set first_user_prefix = "" -%}
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{%- set loop_messages = messages -%}
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{%- endif -%}
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{%- for message in loop_messages -%}
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{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
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{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
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{%- endif -%}
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{%- if (message['role'] == 'assistant') -%}
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{%- set role = "model" -%}
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{%- else -%}
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{%- set role = message['role'] -%}
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{%- endif -%}
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{{ '<start_of_turn>' + role + '
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' + (first_user_prefix if loop.first else "") }}
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{%- if message['content'] is string -%}
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{{ message['content'] | trim }}
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{%- elif message['content'] is iterable -%}
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{%- for item in message['content'] -%}
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{%- if item['type'] == 'image' -%}
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{{ '<start_of_image>' }}
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{%- elif item['type'] == 'text' -%}
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{{ item['text'] | trim }}
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{%- endif -%}
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{%- endfor -%}
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{%- else -%}
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{{ raise_exception("Invalid content type") }}
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{%- endif -%}
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{{ '<end_of_turn>
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' }}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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{{'<start_of_turn>model
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'}}
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{%- endif -%}
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config.json
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|
| 859 |
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|
| 860 |
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|
| 861 |
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|
| 862 |
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|
| 863 |
+
"vision_tower.vision_model.encoder.layers.8.mlp.fc2.weight": "pytorch_model-00001-of-00005.bin",
|
| 864 |
+
"vision_tower.vision_model.encoder.layers.8.self_attn.k_proj.bias": "pytorch_model-00001-of-00005.bin",
|
| 865 |
+
"vision_tower.vision_model.encoder.layers.8.self_attn.k_proj.weight": "pytorch_model-00001-of-00005.bin",
|
| 866 |
+
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|
| 867 |
+
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|
| 868 |
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|
| 869 |
+
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|
| 870 |
+
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|
| 871 |
+
"vision_tower.vision_model.encoder.layers.8.self_attn.v_proj.weight": "pytorch_model-00001-of-00005.bin",
|
| 872 |
+
"vision_tower.vision_model.encoder.layers.9.layer_norm1.bias": "pytorch_model-00001-of-00005.bin",
|
| 873 |
+
"vision_tower.vision_model.encoder.layers.9.layer_norm1.weight": "pytorch_model-00001-of-00005.bin",
|
| 874 |
+
"vision_tower.vision_model.encoder.layers.9.layer_norm2.bias": "pytorch_model-00001-of-00005.bin",
|
| 875 |
+
"vision_tower.vision_model.encoder.layers.9.layer_norm2.weight": "pytorch_model-00001-of-00005.bin",
|
| 876 |
+
"vision_tower.vision_model.encoder.layers.9.mlp.fc1.bias": "pytorch_model-00001-of-00005.bin",
|
| 877 |
+
"vision_tower.vision_model.encoder.layers.9.mlp.fc1.weight": "pytorch_model-00001-of-00005.bin",
|
| 878 |
+
"vision_tower.vision_model.encoder.layers.9.mlp.fc2.bias": "pytorch_model-00001-of-00005.bin",
|
| 879 |
+
"vision_tower.vision_model.encoder.layers.9.mlp.fc2.weight": "pytorch_model-00001-of-00005.bin",
|
| 880 |
+
"vision_tower.vision_model.encoder.layers.9.self_attn.k_proj.bias": "pytorch_model-00001-of-00005.bin",
|
| 881 |
+
"vision_tower.vision_model.encoder.layers.9.self_attn.k_proj.weight": "pytorch_model-00001-of-00005.bin",
|
| 882 |
+
"vision_tower.vision_model.encoder.layers.9.self_attn.out_proj.bias": "pytorch_model-00001-of-00005.bin",
|
| 883 |
+
"vision_tower.vision_model.encoder.layers.9.self_attn.out_proj.weight": "pytorch_model-00001-of-00005.bin",
|
| 884 |
+
"vision_tower.vision_model.encoder.layers.9.self_attn.q_proj.bias": "pytorch_model-00001-of-00005.bin",
|
| 885 |
+
"vision_tower.vision_model.encoder.layers.9.self_attn.q_proj.weight": "pytorch_model-00001-of-00005.bin",
|
| 886 |
+
"vision_tower.vision_model.encoder.layers.9.self_attn.v_proj.bias": "pytorch_model-00001-of-00005.bin",
|
| 887 |
+
"vision_tower.vision_model.encoder.layers.9.self_attn.v_proj.weight": "pytorch_model-00001-of-00005.bin",
|
| 888 |
+
"vision_tower.vision_model.post_layernorm.bias": "pytorch_model-00001-of-00005.bin",
|
| 889 |
+
"vision_tower.vision_model.post_layernorm.weight": "pytorch_model-00001-of-00005.bin"
|
| 890 |
+
}
|
| 891 |
+
}
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,33 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"boi_token": "<start_of_image>",
|
| 3 |
+
"bos_token": {
|
| 4 |
+
"content": "<bos>",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false
|
| 9 |
+
},
|
| 10 |
+
"eoi_token": "<end_of_image>",
|
| 11 |
+
"eos_token": {
|
| 12 |
+
"content": "<eos>",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false
|
| 17 |
+
},
|
| 18 |
+
"image_token": "<image_soft_token>",
|
| 19 |
+
"pad_token": {
|
| 20 |
+
"content": "<eos>",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false
|
| 25 |
+
},
|
| 26 |
+
"unk_token": {
|
| 27 |
+
"content": "<unk>",
|
| 28 |
+
"lstrip": false,
|
| 29 |
+
"normalized": false,
|
| 30 |
+
"rstrip": false,
|
| 31 |
+
"single_word": false
|
| 32 |
+
}
|
| 33 |
+
}
|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:cc88d65cae198c51f7f16a3b1eed862b122130e040d76139a3cac445385989cb
|
| 3 |
+
size 33384821
|
tokenizer.model
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
|
| 3 |
+
size 4689074
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"tokenizer_class": "GemmaTokenizer",
|
| 3 |
+
"clean_up_tokenization_spaces": true,
|
| 4 |
+
"eos_token": "<eos>",
|
| 5 |
+
"pad_token": "<eos>",
|
| 6 |
+
"unk_token": "<unk>",
|
| 7 |
+
"use_default_system_prompt": false
|
| 8 |
+
}
|