README.md

---
pipeline_tag: text2text-generation
language:
- fr
metrics:
- accuracy
- code_eval
- bleu
base_model: meta-llama/Llama-3.3-70B-Instruct
library_name: transformers.js
datasets:
- fka/awesome-chatgpt-prompts
- cognitivecomputations/dolphin-r1
---

# Nom du Modèle

**Description :**  
Ce modèle est un adaptateur basé sur `meta-llama/Llama-3.3-70B-Instruct`, fine-tuné pour générer du texte en français pour des applications spécifiques (ex. : analyse SEO).

**Utilisation :**  
Vous pouvez interroger ce modèle via l'Inference API pour obtenir des réponses à vos questions. Par exemple :
```javascript
const fetch = require('node-fetch');
async function queryModel(prompt) {
  const API_URL = "https://api-inference.huggingface.co/models/your-username/your-custom-model";
  const API_TOKEN = "YOUR_HUGGINGFACE_API_TOKEN";
  const response = await fetch(API_URL, {
    method: "POST",
    headers: {
      "Authorization": `Bearer ${API_TOKEN}`,
      "Content-Type": "application/json"
    },
    body: JSON.stringify({ inputs: prompt })
  });
  return await response.json();
}
queryModel("Bonjour, comment améliorer mon SEO ?")
  .then(console.log)
  .catch(console.error);


---
# Model Card for Model ID

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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

### Model Description

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## Uses

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### Direct Use

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

### Model Architecture and Objective

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## Citation [optional]

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