Instructions to use lightblue/jod with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lightblue/jod with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lightblue/jod")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("lightblue/jod") model = AutoModelForCausalLM.from_pretrained("lightblue/jod") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use lightblue/jod with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lightblue/jod" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lightblue/jod", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lightblue/jod
- SGLang
How to use lightblue/jod 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 "lightblue/jod" \ --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": "lightblue/jod", "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 "lightblue/jod" \ --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": "lightblue/jod", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use lightblue/jod with Docker Model Runner:
docker model run hf.co/lightblue/jod
Update README.md
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README.md
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This model was trained using the ChatML format, so it should be used for inference using the ChatML chatbot format.
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We chose this format as the base model ([Open-Orca/Mistral-7B-SlimOrca](https://huggingface.co/Open-Orca/Mistral-7B-SlimOrca)) was trained with this format, and we find the chatbot format more compelling for practical use compared to the Alpaca style instruction format.
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We trained for 1 epoch using the following Axolotl config. (Early stopping was not performed during our training.)
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<details><summary>Axolotl config .yaml</summary>
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unk_token: "<unk>"
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</details>
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This model was trained using the ChatML format, so it should be used for inference using the ChatML chatbot format.
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We chose this format as the base model ([Open-Orca/Mistral-7B-SlimOrca](https://huggingface.co/Open-Orca/Mistral-7B-SlimOrca)) was trained with this format, and we find the chatbot format more compelling for practical use compared to the Alpaca style instruction format.
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We trained for 1 epoch using the following Axolotl config. (Early stopping was not performed during our training.)
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<details><summary>Axolotl config .yaml</summary>
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unk_token: "<unk>"
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```
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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