Instructions to use Undi95/MLewd-L2-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Undi95/MLewd-L2-13B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Undi95/MLewd-L2-13B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Undi95/MLewd-L2-13B") model = AutoModelForCausalLM.from_pretrained("Undi95/MLewd-L2-13B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Undi95/MLewd-L2-13B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Undi95/MLewd-L2-13B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Undi95/MLewd-L2-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Undi95/MLewd-L2-13B
- SGLang
How to use Undi95/MLewd-L2-13B 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 "Undi95/MLewd-L2-13B" \ --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": "Undi95/MLewd-L2-13B", "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 "Undi95/MLewd-L2-13B" \ --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": "Undi95/MLewd-L2-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Undi95/MLewd-L2-13B with Docker Model Runner:
docker model run hf.co/Undi95/MLewd-L2-13B
Commit History
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Adding Evaluation Results (#3)
- Adding Evaluation Results (95b40c1275efd9e209f96109edfe5e13c57c5ee0) Co-authored-by: Open LLM Leaderboard PR Bot <leaderboard-pr-bot@users.noreply.huggingface.co>d874810
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Update README.md
ec22f33 -
Update README.md (#1)
- Update README.md (22ff1cf2fee7d5533c5f391d26c4312837db9877) Co-authored-by: dfadf <llama-anon@users.noreply.huggingface.co>e1c907c
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Update README.md
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Update README.md
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Create README.md
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Upload tokenizer.model
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Upload tokenizer
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Upload LlamaForCausalLM
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initial commit
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