Instructions to use jpacifico/French-Alpaca-7B-Instruct-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jpacifico/French-Alpaca-7B-Instruct-beta with Transformers:

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

pipe = pipeline("text-generation", model="jpacifico/French-Alpaca-7B-Instruct-beta")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jpacifico/French-Alpaca-7B-Instruct-beta")
model = AutoModelForCausalLM.from_pretrained("jpacifico/French-Alpaca-7B-Instruct-beta")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jpacifico/French-Alpaca-7B-Instruct-beta with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jpacifico/French-Alpaca-7B-Instruct-beta"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jpacifico/French-Alpaca-7B-Instruct-beta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jpacifico/French-Alpaca-7B-Instruct-beta

SGLang

How to use jpacifico/French-Alpaca-7B-Instruct-beta 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 "jpacifico/French-Alpaca-7B-Instruct-beta" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jpacifico/French-Alpaca-7B-Instruct-beta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "jpacifico/French-Alpaca-7B-Instruct-beta" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jpacifico/French-Alpaca-7B-Instruct-beta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jpacifico/French-Alpaca-7B-Instruct-beta with Docker Model Runner:
```
docker model run hf.co/jpacifico/French-Alpaca-7B-Instruct-beta
```

Model Card for Model ID

A 7B language model. Good in French.

Model Description

The French-Alpaca is a 7.24B params LLM model based on the Mistral-7B-Instruct-v0.2 foundation model,
fine-tuned from the original French-Alpaca-dataset entirely generated with OpenAI GPT-3.5-turbo.

French-Alpaca is a general model and can itself be finetuned to be specialized for specific use cases.

The fine-tuning method is inspired from https://crfm.stanford.edu/2023/03/13/alpaca.html

Usage & Test

#!pip install transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "jpacifico/French-Alpaca-7B-Instruct-beta"
messages = [{"role": "user", "content": "Rédige un article sur la fin des vendanges dans le Mâconnais."}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

You can test French-Alpaca with this dedicated and compatible colab notebook (with free T4 GPU) :
https://github.com/jpacifico/French-Alpaca/blob/main/French_Alpaca_inference_test_colab.ipynb

This quantized GGUF version availabe is available here : https://huggingface.co/jpacifico/French-Alpaca-7B-Instruct-beta-GGUF
It can be used on a CPU device, compatible with llama.cpp and LM Studio (cf screenshot below).

Limitations

The French-Alpaca model is a quick demonstration that a base 7B model can be easily fine-tuned to specialize in a particular language. It does not have any moderation mechanisms.

Developed by: Jonathan Pacifico, 2024
Model type: LLM
Language(s) (NLP): French
License: Apache 2.0
Finetuned from model: mistralai/Mistral-7B-Instruct-v0.2

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Safetensors

Model size

7B params

Tensor type

F16

Model tree for jpacifico/French-Alpaca-7B-Instruct-beta

Merges

3 models

Dataset used to train jpacifico/French-Alpaca-7B-Instruct-beta

Collection including jpacifico/French-Alpaca-7B-Instruct-beta

French-Alpaca 🦙

Collection

French-Alpaca models Family • 10 items • Updated Oct 20, 2025 • 2