Instructions to use torchao-dev/opt-125m-float8dq-row-0.13-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use torchao-dev/opt-125m-float8dq-row-0.13-dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="torchao-dev/opt-125m-float8dq-row-0.13-dev")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("torchao-dev/opt-125m-float8dq-row-0.13-dev") model = AutoModelForCausalLM.from_pretrained("torchao-dev/opt-125m-float8dq-row-0.13-dev") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use torchao-dev/opt-125m-float8dq-row-0.13-dev with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "torchao-dev/opt-125m-float8dq-row-0.13-dev" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "torchao-dev/opt-125m-float8dq-row-0.13-dev", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/torchao-dev/opt-125m-float8dq-row-0.13-dev
- SGLang
How to use torchao-dev/opt-125m-float8dq-row-0.13-dev 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 "torchao-dev/opt-125m-float8dq-row-0.13-dev" \ --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": "torchao-dev/opt-125m-float8dq-row-0.13-dev", "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 "torchao-dev/opt-125m-float8dq-row-0.13-dev" \ --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": "torchao-dev/opt-125m-float8dq-row-0.13-dev", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use torchao-dev/opt-125m-float8dq-row-0.13-dev with Docker Model Runner:
docker model run hf.co/torchao-dev/opt-125m-float8dq-row-0.13-dev
- Xet hash:
- 1fdb2673aad9f4f4644df3c4930b0e85f9a4fdb362dc0dfbb4878ebab86ae9d4
- Size of remote file:
- 166 MB
- SHA256:
- b7fc20741c92572bacfe09da100aff2ed8baa3c0912e0cbd00bc44fdd8bdc946
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