Instructions to use Jinx-org/Jinx-gpt-oss-20b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jinx-org/Jinx-gpt-oss-20b with Transformers:

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

pipe = pipeline("text-generation", model="Jinx-org/Jinx-gpt-oss-20b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jinx-org/Jinx-gpt-oss-20b")
model = AutoModelForCausalLM.from_pretrained("Jinx-org/Jinx-gpt-oss-20b", 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 Jinx-org/Jinx-gpt-oss-20b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jinx-org/Jinx-gpt-oss-20b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jinx-org/Jinx-gpt-oss-20b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jinx-org/Jinx-gpt-oss-20b

SGLang

How to use Jinx-org/Jinx-gpt-oss-20b 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 "Jinx-org/Jinx-gpt-oss-20b" \
    --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": "Jinx-org/Jinx-gpt-oss-20b",
		"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 "Jinx-org/Jinx-gpt-oss-20b" \
        --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": "Jinx-org/Jinx-gpt-oss-20b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jinx-org/Jinx-gpt-oss-20b with Docker Model Runner:
```
docker model run hf.co/Jinx-org/Jinx-gpt-oss-20b
```

Why did you make this model so good at math, an already uncensored topic?

by treehugg3 - opened Aug 16, 2025

Discussion

treehugg3

Aug 16, 2025

As you note in the charts, this model is exceptionally good at math reasoning, outperforming gpt-oss-20b by 3.5 points on LiveMathBench.

This is strange because math reasoning is not the main focus of most "uncensored model" requests. If you have a math problem, no model is going to refuse solving it.

I am curious whether this "training on an uncensored reasoning topic like math" was a necessary component of your methods or if it was just to make the model excel in that benchmark.

Jeol

Jinx org Aug 18, 2025

Thanks for your question.
We test Jinx on the common ability dimension to verify if it is capable as the original version on normal tasks.
I think we do not enhance gpt-oss in math. The improvement in math can be viewed as evaluation noise.

Best,
Jinx Team

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