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ECE-PRYMMAL-0.5B-FT-EnhancedMUSREnsembleV3

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 ---
 library_name: transformers
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 ---
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 ---
 library_name: transformers
+tags:
+- musr
+- question-answering
+- reasoning
+- multi-source
+- qwen
+- enhanced-ensemble
+language:
+- en
+license: apache-2.0
+metrics:
+- accuracy: 1.0
+- confidence: 1.1167
+- source_usage: 0.9972
+datasets:
+- allenai/qasc
 ---
+# Model Card for ECE-PRYMMAL-0.5B-FT-EnhancedMUSREnsembleV3
 ## Model Details
 ### Model Description
+Ce modèle est une version hautement optimisée de Qwen-0.5B, spécialement conçue pour exceller dans le raisonnement multi-source (MUSR). Il représente la troisième version de notre architecture d'ensemble améliorée, atteignant des performances exceptionnelles sur le benchmark MUSR.
+- **Developed by:** matouLeLoup
+- **Model type:** Auto-regressive language model
+- **Language(s):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** Qwen/Qwen2-0.5B
+## Training and Evaluation
+### Training Data
+- Base model: Qwen-0.5B
+- Fine-tuning dataset: allenai/qasc
+### Evaluation Results
+Tested on 500 samples from QASC validation set:
+- Accuracy: 100%
+- Confidence: 1.1167 (±0.0171)
+- Source Usage: 99.72%
+- Response Length: 170.5 words (±22.8)
+- Reasoning Steps: 1.36 average
+Confidence Distribution:
+- >1.1 : 95.8%
+- 1.0-1.1 : 4.2%
+- <1.0 : 0%
 ## Uses
 ### Direct Use
+Ce modèle est optimisé pour :
+- Questions-réponses multi-sources
+- Raisonnement logique
+- Analyse et synthèse de documents
+- Systèmes d'aide à la décision
+- Applications éducatives
+### How to Get Started
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("matouLeLoup/ECE-PRYMMAL-0.5B-FT-EnhancedMUSREnsembleV3")
+tokenizer = AutoTokenizer.from_pretrained("matouLeLoup/ECE-PRYMMAL-0.5B-FT-EnhancedMUSREnsembleV3")
+# Format de prompt optimal
+prompt = f"""Context:
+Fact 1: {fact1}
+Fact 2: {fact2}
+Question: {question}
+Choices:
+{choices}
+Instructions:
+1. Analyze both facts carefully
+2. Connect the information
+3. Choose the letter (A-H) that best answers the question
+4. Explain your reasoning
+Reasoned Answer:"""
+# Génération
+inputs = tokenizer(prompt, return_tensors="pt").to(device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=150,
+    num_beams=5,
+    temperature=0.6,
+    no_repeat_ngram_size=3
+)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+# Training details
+Training Procedure
+Training Hyperparameters
+Learning rate: 2e-5
+  Batch size: 32
+  Weight decay: 0.1
+  Warmup steps: 0
+  Scheduler: polynomial
+  Training regime: bf16 mixed precision
+# Evaluation Procedure
+  Tested on 500 random samples from QASC validation set
+  Evaluated for accuracy, confidence, and source usage
+  Detailed analysis of reasoning steps and response quality
+# Limitations and Bias
+  Optimisé spécifiquement pour le format MUSR
+  Nécessite une structuration précise des prompts
+  Conçu pour des questions à choix multiples avec raisonnement
+# Technical Specifications
+  Base model: Qwen-0.5B
+  Enhanced with optimized generation parameters
+  Uses letter-based answer format (A-H)
+# Generation config
+generation_config = {
+    "max_new_tokens": 150,
+    "num_beams": 5,
+    "temperature": 0.6,
+    "do_sample": False,
+    "length_penalty": 1.0,
+    "no_repeat_ngram_size": 3
+}
+@misc{PRYMMAL-EnhancedMUSREnsembleV3,
+  author = {matouLeLoup},
+  title = {ECE-PRYMMAL-0.5B-FT-EnhancedMUSREnsembleV3},
+  year = {2024},
+  publisher = {Hugging Face},
+  journal = {Hugging Face Hub},
+  howpublished = {\url{https://huggingface.co/matouLeLoup/ECE-PRYMMAL-0.5B-FT-EnhancedMUSREnsembleV3}}
+}