from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "NidhalFerjani/Llama-3.2-3b-Coffria-Stage-1"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

prompt = "Votre prompt ici"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Use Cases

This model is designed for natural dialogue to assist in document search. It can help users efficiently find the most relevant documents based on natural language queries, making it ideal for applications like document retrieval, research assistance, and knowledge discovery.

Limitations

This model inherits the limitations of the base Llama 3.2 model.

Licence

This model is subject to the same licensing terms as the base Llama 3.2 model.