Instructions to use sharpbai/open_llama_13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sharpbai/open_llama_13b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sharpbai/open_llama_13b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("sharpbai/open_llama_13b") model = AutoModelForCausalLM.from_pretrained("sharpbai/open_llama_13b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use sharpbai/open_llama_13b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sharpbai/open_llama_13b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sharpbai/open_llama_13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/sharpbai/open_llama_13b
- SGLang
How to use sharpbai/open_llama_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 "sharpbai/open_llama_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": "sharpbai/open_llama_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 "sharpbai/open_llama_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": "sharpbai/open_llama_13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use sharpbai/open_llama_13b with Docker Model Runner:
docker model run hf.co/sharpbai/open_llama_13b
Update README.md
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README.md
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@@ -84,7 +84,7 @@ The original LLaMA model was trained for 1 trillion tokens and GPT-J was trained
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| **Task/Metric** | GPT-J 6B | LLaMA 7B | LLaMA 13B | OpenLLaMA 7B | OpenLLaMA 3B | OpenLLaMA 13B |
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| anli_r1/acc | 0.32 | 0.35 | 0.35 | 0.33 | 0.33 | 0.33 |
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| anli_r2/acc | 0.34 | 0.34 | 0.36 | 0.36 | 0.32 | 0.33 |
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| anli_r3/acc | 0.35 | 0.37 | 0.39 | 0.38 | 0.35 | 0.40 |
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| **Task/Metric** | GPT-J 6B | LLaMA 7B | LLaMA 13B | OpenLLaMA 7B | OpenLLaMA 3B | OpenLLaMA 13B |
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| anli_r1/acc | 0.32 | 0.35 | 0.35 | 0.33 | 0.33 | 0.33 |
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| anli_r2/acc | 0.34 | 0.34 | 0.36 | 0.36 | 0.32 | 0.33 |
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| anli_r3/acc | 0.35 | 0.37 | 0.39 | 0.38 | 0.35 | 0.40 |
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