Text Generation
Transformers
Safetensors
gpt_oss
rys
layer-duplication
conversational
8-bit precision
mxfp4
Instructions to use KotshinZ/gpt-oss-120b-rys-0_19-17_35 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KotshinZ/gpt-oss-120b-rys-0_19-17_35 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="KotshinZ/gpt-oss-120b-rys-0_19-17_35") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("KotshinZ/gpt-oss-120b-rys-0_19-17_35") model = AutoModelForCausalLM.from_pretrained("KotshinZ/gpt-oss-120b-rys-0_19-17_35") 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
- vLLM
How to use KotshinZ/gpt-oss-120b-rys-0_19-17_35 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "KotshinZ/gpt-oss-120b-rys-0_19-17_35" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KotshinZ/gpt-oss-120b-rys-0_19-17_35", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/KotshinZ/gpt-oss-120b-rys-0_19-17_35
- SGLang
How to use KotshinZ/gpt-oss-120b-rys-0_19-17_35 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 "KotshinZ/gpt-oss-120b-rys-0_19-17_35" \ --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": "KotshinZ/gpt-oss-120b-rys-0_19-17_35", "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 "KotshinZ/gpt-oss-120b-rys-0_19-17_35" \ --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": "KotshinZ/gpt-oss-120b-rys-0_19-17_35", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use KotshinZ/gpt-oss-120b-rys-0_19-17_35 with Docker Model Runner:
docker model run hf.co/KotshinZ/gpt-oss-120b-rys-0_19-17_35
How to use from
vLLMUse Docker
docker model run hf.co/KotshinZ/gpt-oss-120b-rys-0_19-17_35Quick Links
gpt-oss-120b RYS 0..19,17..35
This repository is a layer-routed RYS variant of openai/gpt-oss-120b.
- Base model revision:
b5c939de8f754692c1647ca79fbf85e8c1e70f8a - Requested path:
0..19,17..35 - Resolved path:
0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35 - Original layers:
36 - Output layers:
39 - Repeated source layers:
17,18,19
No additional quantization was applied while building this repo. The tensor bytes are copied directly from the source checkpoint and only re-indexed into a new layer execution path.
The model config has been updated so model.layers follows the path above. Tokenizer and chat template files are copied from the base repository unchanged.
- Downloads last month
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Model tree for KotshinZ/gpt-oss-120b-rys-0_19-17_35
Base model
openai/gpt-oss-120b
Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "KotshinZ/gpt-oss-120b-rys-0_19-17_35"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KotshinZ/gpt-oss-120b-rys-0_19-17_35", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'