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README.md
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metrics:
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- accuracy
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pipeline_tag: question-answering
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---
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metrics:
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- accuracy
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pipeline_tag: question-answering
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---
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# Model Card for Finetuned Mistral-7B on Orca Math Word Problems
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<!-- Provide a quick summary of what the model is/does. -->
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Ce modèle est une version du Mistral-7B finement entraînée avec la technique QLoRA (Low-Rank Adaptation) sur le jeu de données Orca-Math Word Problems pour résoudre des problèmes mathématiques en langage naturel.
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## Model Details
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### Model Description
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- **Developed by:** Anthropic
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- **Model type:** Modèle de langage causal (causal language model)
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- **Language(s) (NLP):** Anglais
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- **Finetuned from model:** Mistral-7B-v0.1 de MistralAI
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- **License:** [Plus d'informations nécessaires]
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### Model Sources
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- **Repository:** https://huggingface.co/jeanflop/Mistral_Orcas_7B
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## Uses
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### Direct Use
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Le modèle est un modèle de langage causal entraîné sur le jeu de données Orca-Math Word Problems pour résoudre des problèmes mathématiques en langage naturel.
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Pour utiliser le modèle, vous pouvez charger le modèle et le tokenizer à partir des chemins locaux, puis générer une sortie avec la méthode `generate`:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("/path/to/model")
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tokenizer = AutoTokenizer.from_pretrained("/path/to/tokenizer")
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prompt = "Given the coherent answer sentence to a given problem as an input sentence..."
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids
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output = model.generate(input_ids, max_new_tokens=100)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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Training Details
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Training Data
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Dataset: microsoft/orca-math-word-problems-200k
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Training Procedure
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Preprocessing
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def tokenize(prompt):
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result = tokenizer(
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prompt,
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truncation=True,
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max_length=512,
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padding="max_length",
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)
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result['labels'] = result['input_ids'].copy()
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return result
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def generate_and_tokenize_prompt(data_point):
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full_prompt = f"""Given the logical, mathematical, and coherent answer to a given problem as an input sentence. The entry contains all the information needed to answer the question.
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### Question
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{data_point["question"]}
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### Logical sentences answers
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{data_point["answer"]}
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"""
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return tokenize(full_prompt)
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