Instructions to use titiyu/MistralPersonnaliser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use titiyu/MistralPersonnaliser with Transformers:

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

pipe = pipeline("text-generation", model="titiyu/MistralPersonnaliser")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("titiyu/MistralPersonnaliser")
model = AutoModelForCausalLM.from_pretrained("titiyu/MistralPersonnaliser", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use titiyu/MistralPersonnaliser with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/titiyu/MistralPersonnaliser

SGLang

How to use titiyu/MistralPersonnaliser 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 "titiyu/MistralPersonnaliser" \
    --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": "titiyu/MistralPersonnaliser",
		"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 "titiyu/MistralPersonnaliser" \
        --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": "titiyu/MistralPersonnaliser",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use titiyu/MistralPersonnaliser with Docker Model Runner:
```
docker model run hf.co/titiyu/MistralPersonnaliser
```

MistralPersonnaliser / params.json

titiyu

copy du dépot mathstral-7B-v0.1

d1d7b01 over 2 years ago

Raw History Blame Contribute Delete

202 Bytes

{
    "dim": 4096,
    "n_layers": 32,
    "head_dim": 128,
    "hidden_dim": 14336,
    "n_heads": 32,
    "n_kv_heads": 8,
    "norm_eps": 1e-05,
    "vocab_size": 32768,
    "rope_theta": 1000000.0
}

	{
	"dim": 4096,
	"n_layers": 32,
	"head_dim": 128,
	"hidden_dim": 14336,
	"n_heads": 32,
	"n_kv_heads": 8,
	"norm_eps": 1e-05,
	"vocab_size": 32768,
	"rope_theta": 1000000.0
	}