Instructions to use c4tdr0ut/gpt-oss-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use c4tdr0ut/gpt-oss-v2 with Transformers:

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

pipe = pipeline("text-generation", model="c4tdr0ut/gpt-oss-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("c4tdr0ut/gpt-oss-v2")
model = AutoModelForCausalLM.from_pretrained("c4tdr0ut/gpt-oss-v2")
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]:]))

Grok

How to use c4tdr0ut/gpt-oss-v2 with Grok:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

llama-cpp-python

How to use c4tdr0ut/gpt-oss-v2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="c4tdr0ut/gpt-oss-v2",
	filename="mistral-small-24b-instruct-2501.BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use c4tdr0ut/gpt-oss-v2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf c4tdr0ut/gpt-oss-v2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf c4tdr0ut/gpt-oss-v2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf c4tdr0ut/gpt-oss-v2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf c4tdr0ut/gpt-oss-v2:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf c4tdr0ut/gpt-oss-v2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf c4tdr0ut/gpt-oss-v2:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf c4tdr0ut/gpt-oss-v2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf c4tdr0ut/gpt-oss-v2:Q4_K_M

Use Docker

docker model run hf.co/c4tdr0ut/gpt-oss-v2:Q4_K_M

LM Studio
Jan

vLLM

How to use c4tdr0ut/gpt-oss-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "c4tdr0ut/gpt-oss-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "c4tdr0ut/gpt-oss-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/c4tdr0ut/gpt-oss-v2:Q4_K_M

SGLang

How to use c4tdr0ut/gpt-oss-v2 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 "c4tdr0ut/gpt-oss-v2" \
    --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": "c4tdr0ut/gpt-oss-v2",
		"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 "c4tdr0ut/gpt-oss-v2" \
        --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": "c4tdr0ut/gpt-oss-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use c4tdr0ut/gpt-oss-v2 with Ollama:
```
ollama run hf.co/c4tdr0ut/gpt-oss-v2:Q4_K_M
```

Unsloth Studio

How to use c4tdr0ut/gpt-oss-v2 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for c4tdr0ut/gpt-oss-v2 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for c4tdr0ut/gpt-oss-v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for c4tdr0ut/gpt-oss-v2 to start chatting

Docker Model Runner
How to use c4tdr0ut/gpt-oss-v2 with Docker Model Runner:
```
docker model run hf.co/c4tdr0ut/gpt-oss-v2:Q4_K_M
```

Lemonade

How to use c4tdr0ut/gpt-oss-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull c4tdr0ut/gpt-oss-v2:Q4_K_M

Run and chat with the model

lemonade run user.gpt-oss-v2-Q4_K_M

List all available models

lemonade list

gpt-oss-v2

File size: 1,443 Bytes

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FROM mistral-small-24b-instruct-2501.BF16.gguf
TEMPLATE """{{- range $index, $_ := .Messages }}
{{- if eq .Role "system" }}[SYSTEM_PROMPT]{{ .Content }}[/SYSTEM_PROMPT]
{{- else if eq .Role "user" }}
{{- if and (le (len (slice $.Messages $index)) 2) $.Tools }}[AVAILABLE_TOOLS]{{ $.Tools }}[/AVAILABLE_TOOLS]
{{- end }}[INST]{{ .Content }}[/INST]
{{- else if eq .Role "assistant" }}
{{- if .Content }}{{ .Content }}
{{- if not (eq (len (slice $.Messages $index)) 1) }}</s>
{{- end }}
{{- else if .ToolCalls }}[TOOL_CALLS][
{{- range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
{{- end }}]</s>
{{- end }}
{{- else if eq .Role "tool" }}[TOOL_RESULTS]{"content": {{ .Content }}}[/TOOL_RESULTS]
{{- end }}
{{- end }}"""
PARAMETER temperature 0.15
SYSTEM """You are Mistral Small 3, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris. Your knowledge base was last updated on 2023-10-01. When you're not sure about some information, you say that you don't have the information and don't make up anything. If the user's question is not clear, ambiguous, or does not provide enough context for you to accurately answer the question, you do not try to answer it right away and you rather ask the user to clarify their request (e.g. "What are some good restaurants around me?" => "Where are you?" or "When is the next flight to Tokyo" => "Where do you travel from?")"""