Instructions to use PhaaNe/gpt_oss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use PhaaNe/gpt_oss with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("openai/gpt-oss-20b") model = PeftModel.from_pretrained(base_model, "PhaaNe/gpt_oss") - Transformers
How to use PhaaNe/gpt_oss with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PhaaNe/gpt_oss") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("PhaaNe/gpt_oss", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use PhaaNe/gpt_oss with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PhaaNe/gpt_oss" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PhaaNe/gpt_oss", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/PhaaNe/gpt_oss
- SGLang
How to use PhaaNe/gpt_oss 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 "PhaaNe/gpt_oss" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PhaaNe/gpt_oss", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "PhaaNe/gpt_oss" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PhaaNe/gpt_oss", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use PhaaNe/gpt_oss with Docker Model Runner:
docker model run hf.co/PhaaNe/gpt_oss
Download checkpoint-600/optimizer.pt from PhaaNe/gpt_oss: direct link, hf CLI and curl.
- Browser
- Download file 32.6 MB
-
https://huggingface.co/PhaaNe/gpt_oss/resolve/main/checkpoint-600/optimizer.pt
- Command line
-
hf download hf://PhaaNe/gpt_oss/checkpoint-600/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/PhaaNe/gpt_oss/resolve/main/checkpoint-600/optimizer.pt
32.6 MB
- Xet hash:
- e64d396ac52d693b423cba31847e79914aba000d422343db34ac25d5f7e18615
- Size of remote file:
- 32.6 MB
- SHA256:
- 03667b18d622d5c7b1ba016be4875be70fb89abcce234a8c9413ab82100287e4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.