Instructions to use ashercn97/manatee-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ashercn97/manatee-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ashercn97/manatee-7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ashercn97/manatee-7b") model = AutoModelForCausalLM.from_pretrained("ashercn97/manatee-7b") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ashercn97/manatee-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ashercn97/manatee-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ashercn97/manatee-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ashercn97/manatee-7b
- SGLang
How to use ashercn97/manatee-7b 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 "ashercn97/manatee-7b" \ --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": "ashercn97/manatee-7b", "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 "ashercn97/manatee-7b" \ --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": "ashercn97/manatee-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ashercn97/manatee-7b with Docker Model Runner:
docker model run hf.co/ashercn97/manatee-7b
Adding Evaluation Results
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by leaderboard-pr-bot - opened
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Manatee is one of my first projects, so I hope you enjoy using it! To use it, you can either use it through the transformer library or if you have limited memory, you can use the GPTQ version that is on my profile!
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In the future, I plan on fine-tuning higher parameter models or making a better version of Manatee-7b.
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Manatee is one of my first projects, so I hope you enjoy using it! To use it, you can either use it through the transformer library or if you have limited memory, you can use the GPTQ version that is on my profile!
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In the future, I plan on fine-tuning higher parameter models or making a better version of Manatee-7b.
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ashercn97__manatee-7b)
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| Metric | Value |
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| Avg. | 45.29 |
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| ARC (25-shot) | 54.52 |
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| HellaSwag (10-shot) | 78.95 |
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| MMLU (5-shot) | 49.26 |
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| TruthfulQA (0-shot) | 46.77 |
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| Winogrande (5-shot) | 74.51 |
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| GSM8K (5-shot) | 7.05 |
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| DROP (3-shot) | 5.99 |
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