Instructions to use LLMWildling/gpt-oss-220a20b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLMWildling/gpt-oss-220a20b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="LLMWildling/gpt-oss-220a20b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LLMWildling/gpt-oss-220a20b")
model = AutoModelForCausalLM.from_pretrained("LLMWildling/gpt-oss-220a20b")
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 LLMWildling/gpt-oss-220a20b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLMWildling/gpt-oss-220a20b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLMWildling/gpt-oss-220a20b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LLMWildling/gpt-oss-220a20b

SGLang

How to use LLMWildling/gpt-oss-220a20b 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 "LLMWildling/gpt-oss-220a20b" \
    --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": "LLMWildling/gpt-oss-220a20b",
		"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 "LLMWildling/gpt-oss-220a20b" \
        --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": "LLMWildling/gpt-oss-220a20b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LLMWildling/gpt-oss-220a20b with Docker Model Runner:
```
docker model run hf.co/LLMWildling/gpt-oss-220a20b
```

gpt-oss-220a20b

gpt-oss-220a20b is a 220B-class expanded MoE model derived from openai/gpt-oss-120b. It is a new release in this model line, built for agentic software engineering, repository work, OpenAI-compatible tool use, and math-assisted coding.

The model adds roughly 100B-class specialist MoE capacity to the GPT-OSS 120B base and ships ready to run at top-k=20.

This release was developed on a two-GPU local system using a new training method targeting both continued pretraining and post-training for expanded MoE models.

Highlights

220B-class expanded MoE model.
About 20B active parameters per token at the shipped top-k=20 setting.
248 total expert rows: 128 base rows plus 120 added specialist rows.
Ships ready for top-20 MoE inference.
Tested primarily with vLLM OpenAI-compatible Chat Completions tool calling.
Developed for coding agents, repository exploration, SWE tasks, tool-using automation, and math-assisted coding.

Specialist Capacity Mix

The added specialist capacity is SWE-heavy, with agentic/tool-use conditioning across the specialist expansion. By primary expert role:

Specialist area	Added expert rows	Share of added rows
SWE / repository / coding	72	60.0%
Agentic / sequential tool-use	32	26.7%
Math / reasoning	16	13.3%

These percentages describe the high-level expert allocation.

Model Details

Base model: openai/gpt-oss-120b
Model type: expanded MoE
Quantization/runtime format: MXFP4
Total expert rows: 248
Added specialist rows: 120
Default active experts: top-k=20
Config fields: num_experts_per_tok=20, experts_per_token=20
Recommended serving stack: vLLM with GPT-OSS reasoning parser and OpenAI tool-call parser

Recommended Serving

vllm serve /path/to/gpt-oss-220a20b \
  --served-model-name vllm/doobee \
  --tensor-parallel-size 2 \
  --max-model-len 131072 \
  --gpu-memory-utilization 0.95 \
  --trust-remote-code \
  --reasoning-parser openai_gptoss \
  --tool-call-parser openai \
  --enable-auto-tool-choice

The model config sets num_experts_per_tok=20 and experts_per_token=20, so no top-k override is needed for runtimes that respect the config.

Recommended Sampling

Recommended temperature range: 0.7-1.0.

Tool Use

This model was selected for release after local OpenAI-compatible tool-use testing at top-k=20.

For best results, use real OpenAI-compatible tool definitions through the vLLM Chat Completions or Responses-compatible path. Avoid relying on raw text parsing of tool calls.

Intended Use

gpt-oss-220a20b is intended for:

agentic software engineering workflows
repository exploration and codebase summarization
SWE-style debugging and implementation tasks
OpenAI-compatible tool-use agents
math-assisted coding and reasoning
production agent and developer-tool deployments

Limitations

This is an independent expanded model derived from GPT-OSS 120B, not an official OpenAI release.
Tool-calling behavior depends strongly on the serving stack, chat template, tool schema, and sampling settings.

Development Note

This model was produced on a two-GPU local setup using a new training method for MoE expansion targeting continued pretraining and post-training.

Downloads last month: 26

Safetensors

Model size

231B params

Tensor type

BF16

F32

Model tree for LLMWildling/gpt-oss-220a20b

Base model

openai/gpt-oss-120b

Quantized

(109)

this model