Instructions to use jpacifico/French-Alpaca-Phi-3-mini-128k-instruct-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jpacifico/French-Alpaca-Phi-3-mini-128k-instruct-beta with Transformers:

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

pipe = pipeline("text-generation", model="jpacifico/French-Alpaca-Phi-3-mini-128k-instruct-beta", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jpacifico/French-Alpaca-Phi-3-mini-128k-instruct-beta", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("jpacifico/French-Alpaca-Phi-3-mini-128k-instruct-beta", trust_remote_code=True)
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

vLLM

How to use jpacifico/French-Alpaca-Phi-3-mini-128k-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-Phi-3-mini-128k-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-Phi-3-mini-128k-instruct-beta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jpacifico/French-Alpaca-Phi-3-mini-128k-instruct-beta

SGLang

How to use jpacifico/French-Alpaca-Phi-3-mini-128k-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-Phi-3-mini-128k-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-Phi-3-mini-128k-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-Phi-3-mini-128k-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-Phi-3-mini-128k-instruct-beta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jpacifico/French-Alpaca-Phi-3-mini-128k-instruct-beta with Docker Model Runner:
```
docker model run hf.co/jpacifico/French-Alpaca-Phi-3-mini-128k-instruct-beta
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for Model ID

French-Alpaca based on microsoft/Phi-3-mini-128k-instruct
128k is the context length (in tokens)

Model Description

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

128k version not yet supported by llama.cpp
4k GGUF version available : https://huggingface.co/jpacifico/French-Alpaca-Phi-3-GGUF

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

torch.random.manual_seed(0)

model = AutoModelForCausalLM.from_pretrained(
    "microsoft/Phi-3-mini-4k-instruct", 
    device_map="cuda", 
    torch_dtype="auto", 
    trust_remote_code=True, 
)
tokenizer = AutoTokenizer.from_pretrained("jpacifico/French-Alpaca-Phi-3-mini-128k-instruct-v1.0")

messages = [
    {"role": "system", "content": "Vous êtes un assistant numérique serviable. Veuillez fournir des informations sûres, éthiques et précises à l'utilisateur."},
    {"role": "user", "content": "Pouvez-vous fournir des façons de manger des combinaisons de bananes et de fruits du dragon ?"},
    {"role": "assistant", "content": "Bien sûr ! Voici quelques façons de manger des bananes et des fruits du dragon ensemble : 1. Smoothie à la banane et au fruit du dragon : mixez des bananes et des fruits du dragon avec un peu de lait et de miel. 2. Salade de banane et de fruit du dragon : mélangez des bananes tranchées et des fruits du dragon avec un peu de jus de citron et de miel."},
    {"role": "user", "content": "Et pour résoudre une équation 2x + 3 = 7 ?"},
]

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
)

generation_args = {
    "max_new_tokens": 500,
    "return_full_text": False,
    "temperature": 0.0,
    "do_sample": False,
}

output = pipe(messages, **generation_args)
print(output[0]['generated_text'])

Limitations

The French-Alpaca model is a quick demonstration that a 3B base 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: MIT

Downloads last month: 19

Safetensors

Model size

4B params

Tensor type

F16

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Quantizations

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Dataset used to train jpacifico/French-Alpaca-Phi-3-mini-128k-instruct-beta

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French-Alpaca 🦙

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