Instructions to use jondurbin/airoboros-l2-13b-gpt4-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jondurbin/airoboros-l2-13b-gpt4-2.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jondurbin/airoboros-l2-13b-gpt4-2.0")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jondurbin/airoboros-l2-13b-gpt4-2.0") model = AutoModelForCausalLM.from_pretrained("jondurbin/airoboros-l2-13b-gpt4-2.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jondurbin/airoboros-l2-13b-gpt4-2.0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jondurbin/airoboros-l2-13b-gpt4-2.0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jondurbin/airoboros-l2-13b-gpt4-2.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jondurbin/airoboros-l2-13b-gpt4-2.0
- SGLang
How to use jondurbin/airoboros-l2-13b-gpt4-2.0 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 "jondurbin/airoboros-l2-13b-gpt4-2.0" \ --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": "jondurbin/airoboros-l2-13b-gpt4-2.0", "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 "jondurbin/airoboros-l2-13b-gpt4-2.0" \ --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": "jondurbin/airoboros-l2-13b-gpt4-2.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jondurbin/airoboros-l2-13b-gpt4-2.0 with Docker Model Runner:
docker model run hf.co/jondurbin/airoboros-l2-13b-gpt4-2.0
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README.md
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#### Llama-2 models
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Base model has a custom Meta license:
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- See the LICENSE.txt file attached for the original license
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The fine-tuning data was generated by OpenAI API calls to gpt-4, via [airoboros](https://github.com/jondurbin/airoboros)
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Your best bet is probably to avoid using this commercially due to the OpenAI API usage.
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Either way, by using this model, you agree to completely idemnify me
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#### Llama-2 models
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Base model has a custom Meta license:
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- See the [meta-license/LICENSE.txt](meta-license/LICENSE.txt) file attached for the original license provided by Meta.
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- See also [meta-license/USE_POLICY.md](meta-license/USE_POLICY.md) and [meta-license/Responsible-Use-Guide.pdf](meta-license/Responsible-Use-Guide.pdf), also provided by Meta.
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The fine-tuning data was generated by OpenAI API calls to gpt-4, via [airoboros](https://github.com/jondurbin/airoboros)
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Your best bet is probably to avoid using this commercially due to the OpenAI API usage.
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Either way, by using this model, you agree to completely idemnify me.
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