---
language:
- fr
base_model:
- LiquidAI/LFM2-700M
pipeline_tag: summarization
datasets:
- CATIE-AQ/frenchSUM
license_name: lfm1.0
co2_eq_emissions: 8
---

## Description
Liquid.AI's [LFM2](https://huggingface.co/LiquidAI/LFM2-700M) model finetuned on [frenchSUM]([hf.co/CATIE-AQ/frenchSUM](https://huggingface.co/datasets/CATIE-AQ/frenchSUM)) dataset to summarize French texts.


## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "CATIE-AQ/LMF2-700M_french_summary"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="bfloat16",
    trust_remote_code=True,
#    attn_implementation="flash_attention_2" <- uncomment on compatible GPU
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

prompt = """Résume l'article suivant :\n""" + "you_text_to_summarize"
tokenizer.padding_side = "left"
tokenizer.truncation_side = "right"

# Apply the chat template to prepare the input
input_ids = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    add_generation_prompt=True,
    return_tensors="pt",
    tokenize=True,
).to(model.device)

# Generate the output from the model
output = model.generate(
    input_ids,
    do_sample=True,
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_new_tokens=2048,
)

summary_text = tokenizer.decode(
    output[0][input_ids.shape[1]:], # Slice the output tensor
    skip_special_tokens=True # Skip special tokens for a cleaner output
)

print(summary_text)
```

## License
[LMF1.0](https://huggingface.co/LiquidAI/LFM2-1.2B/blob/main/LICENSE)