Instructions to use AdrienB134/French-Alpaca-Mistral-7B-v0.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AdrienB134/French-Alpaca-Mistral-7B-v0.3 with Transformers:

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

pipe = pipeline("text-generation", model="AdrienB134/French-Alpaca-Mistral-7B-v0.3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("AdrienB134/French-Alpaca-Mistral-7B-v0.3")
model = AutoModelForMultimodalLM.from_pretrained("AdrienB134/French-Alpaca-Mistral-7B-v0.3")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AdrienB134/French-Alpaca-Mistral-7B-v0.3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AdrienB134/French-Alpaca-Mistral-7B-v0.3"
# 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-Mistral-7B-v0.3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AdrienB134/French-Alpaca-Mistral-7B-v0.3

SGLang

How to use AdrienB134/French-Alpaca-Mistral-7B-v0.3 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-Mistral-7B-v0.3" \
    --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-Mistral-7B-v0.3",
		"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-Mistral-7B-v0.3" \
        --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-Mistral-7B-v0.3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use AdrienB134/French-Alpaca-Mistral-7B-v0.3 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-Mistral-7B-v0.3 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-Mistral-7B-v0.3 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-Mistral-7B-v0.3 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="AdrienB134/French-Alpaca-Mistral-7B-v0.3",
    max_seq_length=2048,
)

Docker Model Runner
How to use AdrienB134/French-Alpaca-Mistral-7B-v0.3 with Docker Model Runner:
```
docker model run hf.co/AdrienB134/French-Alpaca-Mistral-7B-v0.3
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Uploaded model

Developed by: AdrienB134
License: apache-2.0
Finetuned from model : unsloth/mistral-7b-v0.3

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

How to use

from unsloth import FastLanguageModel
import torch

max_seq_length = 32_768 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = False # Use 4bit quantization to reduce memory usage. Can be True.


model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "AdrienB134/French-Alpaca-Mistral-7B-v0.3", 
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

alpaca_prompt = """Ci-dessous tu trouveras une instruction qui décrit une tâche, accompagnée d'un contexte qui donne plus d'informations. Ecrit une réponse appropriée à l'instruction.
### Instruction:
{}

### Contexte:
{}

### Response:
{}"""

FastLanguageModel.for_inference(model) # Enable native 2x faster inference
inputs = tokenizer(
[
    alpaca_prompt.format(
        "Continue la série de fibonacci.", # instruction
        "1, 1, 2, 3, 5, 8", # contexte
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

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

Downloads last month: 26

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for AdrienB134/French-Alpaca-Mistral-7B-v0.3

Base model

unsloth/mistral-7b-v0.3

Finetuned

(26)

this model

Quantizations

1 model

AdrienB134
/

French-Alpaca-Mistral-7B-v0.3

Uploaded model

How to use

Model tree for AdrienB134/French-Alpaca-Mistral-7B-v0.3

Dataset used to train AdrienB134/French-Alpaca-Mistral-7B-v0.3