Instructions to use Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience with Transformers:

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

pipe = pipeline("text-generation", model="Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience

SGLang

How to use Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience 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 "Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience" \
    --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": "Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience",
		"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 "Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience" \
        --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": "Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience with Docker Model Runner:
```
docker model run hf.co/Finisha-F-scratch/Charlotte-Texture4b.x19.1-efficience
```

Charlotte-Texture4b.x19.1-efficience / tokenizer_config.json

Clemylia

Mise à jour Melta : Optimisation RAM et 1-bit

10f66cc verified 8 months ago

Raw History Blame

151 Bytes

	{
	"backend": "tokenizers",
	"model_max_length": 1000000000000000019884624838656,
	"pad_token": "[PAD]",
	"tokenizer_class": "TokenizersBackend"
	}