Instructions to use kuleshov/opt-30b-3bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kuleshov/opt-30b-3bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="kuleshov/opt-30b-3bit")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("kuleshov/opt-30b-3bit") model = AutoModelForCausalLM.from_pretrained("kuleshov/opt-30b-3bit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use kuleshov/opt-30b-3bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kuleshov/opt-30b-3bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kuleshov/opt-30b-3bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/kuleshov/opt-30b-3bit
- SGLang
How to use kuleshov/opt-30b-3bit 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 "kuleshov/opt-30b-3bit" \ --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": "kuleshov/opt-30b-3bit", "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 "kuleshov/opt-30b-3bit" \ --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": "kuleshov/opt-30b-3bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use kuleshov/opt-30b-3bit with Docker Model Runner:
docker model run hf.co/kuleshov/opt-30b-3bit
Download quantized_weights.pt from kuleshov/opt-30b-3bit: direct link, hf CLI and curl.
- Browser
- Download file 13 GB
-
https://huggingface.co/kuleshov/opt-30b-3bit/resolve/main/quantized_weights.pt
- Command line
-
hf download hf://kuleshov/opt-30b-3bit/quantized_weights.pt
-
curl -L -o quantized_weights.pt https://huggingface.co/kuleshov/opt-30b-3bit/resolve/main/quantized_weights.pt
13 GB
- Xet hash:
- a28911cc12dd97185f40f685cdd5b59688dbc80e79f1e4523701437e84f80cd7
- Size of remote file:
- 13 GB
- SHA256:
- 1131a1821a0b04edfccf0540b076d3ae5b20964bea11dcca63cab500ac9a3937
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