Instructions to use bofenghuang/vigogne-falcon-7b-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bofenghuang/vigogne-falcon-7b-chat with Transformers:

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

pipe = pipeline("text-generation", model="bofenghuang/vigogne-falcon-7b-chat", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("bofenghuang/vigogne-falcon-7b-chat", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bofenghuang/vigogne-falcon-7b-chat with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/bofenghuang/vigogne-falcon-7b-chat

SGLang

How to use bofenghuang/vigogne-falcon-7b-chat 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 "bofenghuang/vigogne-falcon-7b-chat" \
    --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": "bofenghuang/vigogne-falcon-7b-chat",
		"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 "bofenghuang/vigogne-falcon-7b-chat" \
        --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": "bofenghuang/vigogne-falcon-7b-chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bofenghuang/vigogne-falcon-7b-chat with Docker Model Runner:
```
docker model run hf.co/bofenghuang/vigogne-falcon-7b-chat
```

Vigogne

Vigogne-Falcon-7B-Chat: A French Chat Falcon Model

Vigogne-Falcon-7B-Chat is a Falcon-7B model 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

Changelog

All versions are available in branches.

V1.0: Initial release.
V2.0: Expanded training dataset to 419k for better performance.

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
from vigogne.preprocess import generate_inference_chat_prompt

model_name_or_path = "bofenghuang/vigogne-falcon-7b-chat"

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side="right", use_fast=False)
tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(
    model_name_or_path,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
)

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,
    pad_token_id=tokenizer.eos_token_id,
    eos_token_id=tokenizer.eos_token_id,
)
generated_tokens = generated_outputs.sequences[0, input_length:]
generated_text = tokenizer.decode(generated_tokens, skip_special_tokens=True)
print(generated_text)

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.

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Collection including bofenghuang/vigogne-falcon-7b-chat

Vigogne v1.0

Collection

French chat LLMs • 10 items • Updated Nov 12, 2024