Instructions to use win10/GPT-OSS-30B-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use win10/GPT-OSS-30B-Preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="win10/GPT-OSS-30B-Preview") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("win10/GPT-OSS-30B-Preview") model = AutoModelForCausalLM.from_pretrained("win10/GPT-OSS-30B-Preview", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use win10/GPT-OSS-30B-Preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "win10/GPT-OSS-30B-Preview" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "win10/GPT-OSS-30B-Preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/win10/GPT-OSS-30B-Preview
- SGLang
How to use win10/GPT-OSS-30B-Preview 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 "win10/GPT-OSS-30B-Preview" \ --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": "win10/GPT-OSS-30B-Preview", "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 "win10/GPT-OSS-30B-Preview" \ --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": "win10/GPT-OSS-30B-Preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Desktop
- Docker Model Runner
How to use win10/GPT-OSS-30B-Preview with Docker Model Runner:
docker model run hf.co/win10/GPT-OSS-30B-Preview
how you scaled up the parameters?
#1
by jiyintor - opened
Could you introduce how you scaled up the parameters? Did you achieve this by adding more experts?
I'd love to know the answer to this also
The simple answer here is in this model's config.json:
"num_hidden_layers": 36,
versus the stock"num_hidden_layers": 24,
But I'm curious too. Are these layers duplicates or has there been finetuning?
I'd be keen to see a spin-off of this model with an expert pruning to reduce the model's size back down, but with less width, more depth.