Instructions to use PeymanHosseini/Hummingbird with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PeymanHosseini/Hummingbird with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PeymanHosseini/Hummingbird")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("PeymanHosseini/Hummingbird", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use PeymanHosseini/Hummingbird with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PeymanHosseini/Hummingbird" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PeymanHosseini/Hummingbird", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/PeymanHosseini/Hummingbird
- SGLang
How to use PeymanHosseini/Hummingbird 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 "PeymanHosseini/Hummingbird" \ --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": "PeymanHosseini/Hummingbird", "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 "PeymanHosseini/Hummingbird" \ --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": "PeymanHosseini/Hummingbird", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use PeymanHosseini/Hummingbird with Docker Model Runner:
docker model run hf.co/PeymanHosseini/Hummingbird
Update README.md
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language: en
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tags:
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- hummingbird
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- causal-lm
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license: mit
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---
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# Hummingbird 0.0 Release
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journal = {arXiv preprint arXiv:2403.01643},
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year = {2024}
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}
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```
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---
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language: en
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tags:
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- hummingbird
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- causal-lm
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license: mit
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datasets:
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- wikimedia/wikipedia
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- qwedsacf/grade-school-math-instructions
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- HuggingFaceH4/instruction-dataset
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- alespalla/chatbot_instruction_prompts
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- MBZUAI/LaMini-instruction
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- hendrycks/competition_math
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- lighteval/MATH
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- camel-ai/math
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- microsoft/orca-math-word-problems-200k
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pipeline_tag: text-generation
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---
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# Hummingbird 0.0 Release
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journal = {arXiv preprint arXiv:2403.01643},
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year = {2024}
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}
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```
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