Instructions to use jerryyan/TraceML-Labelers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jerryyan/TraceML-Labelers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jerryyan/TraceML-Labelers")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jerryyan/TraceML-Labelers", device_map="auto") - ml-agents
How to use jerryyan/TraceML-Labelers with ml-agents:
mlagents-load-from-hf --repo-id="jerryyan/TraceML-Labelers" --local-dir="./downloads"
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jerryyan/TraceML-Labelers with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jerryyan/TraceML-Labelers" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jerryyan/TraceML-Labelers", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jerryyan/TraceML-Labelers
- SGLang
How to use jerryyan/TraceML-Labelers 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 "jerryyan/TraceML-Labelers" \ --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": "jerryyan/TraceML-Labelers", "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 "jerryyan/TraceML-Labelers" \ --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": "jerryyan/TraceML-Labelers", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jerryyan/TraceML-Labelers with Docker Model Runner:
docker model run hf.co/jerryyan/TraceML-Labelers
Download state/tokenizer.json from jerryyan/TraceML-Labelers: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/jerryyan/TraceML-Labelers/resolve/main/state/tokenizer.json
- Command line
-
hf download hf://jerryyan/TraceML-Labelers/state/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/jerryyan/TraceML-Labelers/resolve/main/state/tokenizer.json
11.4 MB
- Xet hash:
- 693ec4b3922b0bd306bf7b4989e115ffbfeb7b0c08b31bc6d956818c6bb07f61
- Size of remote file:
- 11.4 MB
- SHA256:
- aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.