Instructions to use AgentPublic/LlaMAndement-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AgentPublic/LlaMAndement-7b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="AgentPublic/LlaMAndement-7b")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AgentPublic/LlaMAndement-7b")
model = AutoModelForCausalLM.from_pretrained("AgentPublic/LlaMAndement-7b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AgentPublic/LlaMAndement-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AgentPublic/LlaMAndement-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AgentPublic/LlaMAndement-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AgentPublic/LlaMAndement-7b

SGLang

How to use AgentPublic/LlaMAndement-7b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "AgentPublic/LlaMAndement-7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AgentPublic/LlaMAndement-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "AgentPublic/LlaMAndement-7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AgentPublic/LlaMAndement-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AgentPublic/LlaMAndement-7b with Docker Model Runner:
```
docker model run hf.co/AgentPublic/LlaMAndement-7b
```

LlaMAndement-7b / README.md

JosephGesnouin

Update README.md

c164d43 verified almost 3 years ago

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metadata

license: llama2
language: fr
pipeline_tag: text-generation
inference: false
tags:
  - LLM
  - llama-2
  - finetuned

LLaMandement-7B: A Llama-2-based French Models for Summarization of French Legislative Proposals.

LLaMandement-7B is a French chat LLM, based on LLaMA-2-7B, optimized to summarize of French Legislative Proposals.

Model Details

Developed by: DGFIP :
Model type: An auto-regressive language model based on the transformer architecture
License: Llama 2 Community License Agreement
Finetuned from model: Llama 2
Repository: https://gitlab.adullact.net/dgfip/projets-ia/llamandement
Paper: Technical Report

Prompt Template

The prompt for LLaMandement-7B is based on alpaca template :

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Response:

How to Get Started with the Model

Command line interface: https://github.com/lm-sys/FastChat
APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api

Citation

@article{gesnouin2024llamandement,
  title={LLaMandement: Large Language Models for Summarization of French Legislative Proposals},
  author={Gesnouin, Joseph and Tannier, Yannis and Da Silva, Christophe Gomes and Tapory, Hatim and Brier, Camille and Simon, Hugo and Rozenberg, Raphael and Woehrel, Hermann and Yakaabi, Mehdi El and Binder, Thomas and others},
  journal={arXiv preprint arXiv:2401.16182},
  year={2024}
}