Instructions to use hinny-coder/hinny-coder-6.7b-java-awq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hinny-coder/hinny-coder-6.7b-java-awq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="hinny-coder/hinny-coder-6.7b-java-awq")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("hinny-coder/hinny-coder-6.7b-java-awq") model = AutoModelForCausalLM.from_pretrained("hinny-coder/hinny-coder-6.7b-java-awq") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use hinny-coder/hinny-coder-6.7b-java-awq with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hinny-coder/hinny-coder-6.7b-java-awq" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hinny-coder/hinny-coder-6.7b-java-awq", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/hinny-coder/hinny-coder-6.7b-java-awq
- SGLang
How to use hinny-coder/hinny-coder-6.7b-java-awq 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 "hinny-coder/hinny-coder-6.7b-java-awq" \ --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": "hinny-coder/hinny-coder-6.7b-java-awq", "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 "hinny-coder/hinny-coder-6.7b-java-awq" \ --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": "hinny-coder/hinny-coder-6.7b-java-awq", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use hinny-coder/hinny-coder-6.7b-java-awq with Docker Model Runner:
docker model run hf.co/hinny-coder/hinny-coder-6.7b-java-awq
- Xet hash:
- fd595120b44135a23eae6d84826afb0ef79173c11b8ea20462b0d9240e83f44e
- Size of remote file:
- 3.89 GB
- SHA256:
- 25d41a8782f68f718bb7d6007f311a15cef2ee0bc1675b95dd43c7cfcf919f10
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.