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

Libraries

How to use cublya/gpt-oss-20b-Coding-Distill with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cublya/gpt-oss-20b-Coding-Distill")
model = AutoModelForCausalLM.from_pretrained("cublya/gpt-oss-20b-Coding-Distill")
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 cublya/gpt-oss-20b-Coding-Distill with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/cublya/gpt-oss-20b-Coding-Distill

SGLang

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

Unsloth Studio new

How to use cublya/gpt-oss-20b-Coding-Distill 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 cublya/gpt-oss-20b-Coding-Distill 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 cublya/gpt-oss-20b-Coding-Distill to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cublya/gpt-oss-20b-Coding-Distill to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="cublya/gpt-oss-20b-Coding-Distill",
    max_seq_length=2048,
)

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

gpt-oss-20b-Coding-Distill

This project uses Unsloth for fine-tuning. All training data is converted to OpenAI Harmony format before training, but there may be cases where the output format doesn't conform to the OpenAI Harmony specification.

Do you want to use pre-trained model?

You can download pre-trained data from HuggingFace.

Safetensors repo: midorin-Linux/gpt-oss-20b-Coding-Distill
GGUF repo: midorin-Linux/gpt-oss-20b-Coding-Distill-GGUF

Overview

This project implements a sophisticated multi-phase fine-tuning pipeline for the GPT-OSS-20B model, leveraging conversation data from multiple state-of-the-art AI models to create a balanced, high-performance language model optimized for: