---
language:
- fr
pipeline_tag: text-generation
library_name: transformers
inference: false
tags:
- LLM
- llama
- llama-2
---
# Vigogne-2-7B-Chat: A Llama-2 based French chat model
Vigogne-2-7B-Chat is a model based on [LLaMA-2-7B](https://ai.meta.com/llama) that has been fine-tuned to conduct multi-turn dialogues in French between human user and AI assistant.
For more information, please visit the Github repo: https://github.com/bofenghuang/vigogne
**Usage and License Notices**: Vigogne-2-7B-Chat follows the same usage policy as Llama-2, which can be found [here](https://ai.meta.com/llama/use-policy).
## Usage
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
from vigogne.preprocess import generate_inference_chat_prompt
model_name_or_path = "bofenghuang/vigogne-2-7b-chat"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side="right", use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16, device_map="auto")
user_query = "Expliquez la différence entre DoS et phishing."
prompt = generate_inference_chat_prompt([[user_query, ""]], tokenizer=tokenizer)
input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"].to(model.device)
input_length = input_ids.shape[1]
generated_outputs = model.generate(
input_ids=input_ids,
generation_config=GenerationConfig(
temperature=0.1,
do_sample=True,
repetition_penalty=1.0,
max_new_tokens=512,
),
return_dict_in_generate=True,
)
generated_tokens = generated_outputs.sequences[0, input_length:]
generated_text = tokenizer.decode(generated_tokens, skip_special_tokens=True)
print(generated_text)
```
You can infer this model by using the following Google Colab Notebook.
## Limitations
Vigogne is still under development, and there are many limitations that have to be addressed. Please note that it is possible that the model generates harmful or biased content, incorrect information or generally unhelpful answers.