Instructions to use AdrienB134/French-Alpaca-Croissant-1.3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AdrienB134/French-Alpaca-Croissant-1.3B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="AdrienB134/French-Alpaca-Croissant-1.3B-Instruct")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AdrienB134/French-Alpaca-Croissant-1.3B-Instruct")
model = AutoModelForCausalLM.from_pretrained("AdrienB134/French-Alpaca-Croissant-1.3B-Instruct")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use AdrienB134/French-Alpaca-Croissant-1.3B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AdrienB134/French-Alpaca-Croissant-1.3B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AdrienB134/French-Alpaca-Croissant-1.3B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AdrienB134/French-Alpaca-Croissant-1.3B-Instruct

SGLang

How to use AdrienB134/French-Alpaca-Croissant-1.3B-Instruct 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 "AdrienB134/French-Alpaca-Croissant-1.3B-Instruct" \
    --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": "AdrienB134/French-Alpaca-Croissant-1.3B-Instruct",
		"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 "AdrienB134/French-Alpaca-Croissant-1.3B-Instruct" \
        --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": "AdrienB134/French-Alpaca-Croissant-1.3B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

How to use AdrienB134/French-Alpaca-Croissant-1.3B-Instruct with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for AdrienB134/French-Alpaca-Croissant-1.3B-Instruct to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for AdrienB134/French-Alpaca-Croissant-1.3B-Instruct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AdrienB134/French-Alpaca-Croissant-1.3B-Instruct to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="AdrienB134/French-Alpaca-Croissant-1.3B-Instruct",
    max_seq_length=2048,
)

Docker Model Runner
How to use AdrienB134/French-Alpaca-Croissant-1.3B-Instruct with Docker Model Runner:
```
docker model run hf.co/AdrienB134/French-Alpaca-Croissant-1.3B-Instruct
```

How to use:

from transformers import TextStreamer
from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "AdrienB134/French-Alpaca-Croissant-1.3B-Instruct",
    max_seq_length = 4096,
    dtype = None,
    load_in_4bit = True,
    fix_tokenizer = False,
)

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

FastLanguageModel.for_inference(model) 

inputs = tokenizer(
[
    alpaca_prompt.format(
        "Continue la suite de Fibonnaci", # instruction
        "1, 1, 2, 3, 5, 8", # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)

Uploaded model

Developed by: AdrienB134
License: MIT
Finetuned from model : croissantllm/CroissantLLMBase

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 6

Model tree for AdrienB134/French-Alpaca-Croissant-1.3B-Instruct

Base model

croissantllm/CroissantLLMBase

Finetuned

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/

French-Alpaca-Croissant-1.3B-Instruct

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