Text Generation
Transformers
Safetensors
llama
Merge
mergekit
lazymergekit
davidkim205/Rhea-72b-v0.5
abacusai/Smaug-72B-v0.1
text-generation-inference
Instructions to use MatthieuJ/ECE-TW3-JRGL-V5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MatthieuJ/ECE-TW3-JRGL-V5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MatthieuJ/ECE-TW3-JRGL-V5")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MatthieuJ/ECE-TW3-JRGL-V5") model = AutoModelForCausalLM.from_pretrained("MatthieuJ/ECE-TW3-JRGL-V5") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MatthieuJ/ECE-TW3-JRGL-V5 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MatthieuJ/ECE-TW3-JRGL-V5" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MatthieuJ/ECE-TW3-JRGL-V5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MatthieuJ/ECE-TW3-JRGL-V5
- SGLang
How to use MatthieuJ/ECE-TW3-JRGL-V5 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 "MatthieuJ/ECE-TW3-JRGL-V5" \ --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": "MatthieuJ/ECE-TW3-JRGL-V5", "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 "MatthieuJ/ECE-TW3-JRGL-V5" \ --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": "MatthieuJ/ECE-TW3-JRGL-V5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MatthieuJ/ECE-TW3-JRGL-V5 with Docker Model Runner:
docker model run hf.co/MatthieuJ/ECE-TW3-JRGL-V5
- Xet hash:
- 6ddfefc6fc645549aaa31729dd9e11ea882b72e9b1adf4f7f3444485e01d7eda
- Size of remote file:
- 1.74 GB
- SHA256:
- 8801fd14f9de87788be0ca2d6f6b88603fd9dc1485d4e4c77f09f6489cfc05ff
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