Instructions to use jz07/model_code_style with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jz07/model_code_style with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jz07/model_code_style")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jz07/model_code_style") model = AutoModelForCausalLM.from_pretrained("jz07/model_code_style", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jz07/model_code_style with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jz07/model_code_style" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jz07/model_code_style", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jz07/model_code_style
- SGLang
How to use jz07/model_code_style 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 "jz07/model_code_style" \ --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": "jz07/model_code_style", "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 "jz07/model_code_style" \ --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": "jz07/model_code_style", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Desktop
- Docker Model Runner
How to use jz07/model_code_style with Docker Model Runner:
docker model run hf.co/jz07/model_code_style
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Download README.md from jz07/model_code_style: direct link, hf CLI and curl.
- Browser
- Download file 605 Bytes
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https://huggingface.co/jz07/model_code_style/resolve/refs%2Fpr%2F1/README.md
- Command line
-
hf download hf://jz07/model_code_style@refs/pr/1/README.md
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curl -L -o README.md https://huggingface.co/jz07/model_code_style/resolve/refs%2Fpr%2F1/README.md
605 Bytes
| base_model: unsloth/mistral-nemo-base-2407-bnb-4bit | |
| language: | |
| - en | |
| license: apache-2.0 | |
| tags: | |
| - text-generation-inference | |
| - transformers | |
| - unsloth | |
| - mistral | |
| - trl | |
| - sft | |
| # Uploaded model | |
| - **Developed by:** jz07 | |
| - **License:** apache-2.0 | |
| - **Finetuned from model :** unsloth/mistral-nemo-base-2407-bnb-4bit | |
| This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. | |
| [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) | |