Instructions to use Joetib/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Joetib/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Joetib/results")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Joetib/results") model = AutoModelForCausalLM.from_pretrained("Joetib/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Joetib/results with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Joetib/results" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Joetib/results", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Joetib/results
- SGLang
How to use Joetib/results 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 "Joetib/results" \ --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": "Joetib/results", "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 "Joetib/results" \ --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": "Joetib/results", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Joetib/results with Docker Model Runner:
docker model run hf.co/Joetib/results
Download adapter_model.bin from Joetib/results: direct link, hf CLI and curl.
- Browser
- Download file 109 MB
-
https://huggingface.co/Joetib/results/resolve/main/adapter_model.bin
- Command line
-
hf download hf://Joetib/results/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/Joetib/results/resolve/main/adapter_model.bin
109 MB
- Xet hash:
- d387f923cf9392182b59e5f469e0d559f0127fdcdc9cd1b3dd8151851913cc1c
- Size of remote file:
- 109 MB
- SHA256:
- 4440a713968a916b2eb13f382cc79d3469f3a1bab940b5cbf296803f1742e2e5
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