Instructions to use Pinkstack/DistilGPT-OSS-qwen3-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pinkstack/DistilGPT-OSS-qwen3-4B with Transformers:

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

pipe = pipeline("text-generation", model="Pinkstack/DistilGPT-OSS-qwen3-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Pinkstack/DistilGPT-OSS-qwen3-4B")
model = AutoModelForCausalLM.from_pretrained("Pinkstack/DistilGPT-OSS-qwen3-4B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Pinkstack/DistilGPT-OSS-qwen3-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pinkstack/DistilGPT-OSS-qwen3-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pinkstack/DistilGPT-OSS-qwen3-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Pinkstack/DistilGPT-OSS-qwen3-4B

SGLang

How to use Pinkstack/DistilGPT-OSS-qwen3-4B 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 "Pinkstack/DistilGPT-OSS-qwen3-4B" \
    --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": "Pinkstack/DistilGPT-OSS-qwen3-4B",
		"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 "Pinkstack/DistilGPT-OSS-qwen3-4B" \
        --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": "Pinkstack/DistilGPT-OSS-qwen3-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Pinkstack/DistilGPT-OSS-qwen3-4B with Docker Model Runner:
```
docker model run hf.co/Pinkstack/DistilGPT-OSS-qwen3-4B
```

Pinkstack commited on Sep 19, 2025

Commit

96ea5c8

verified ·

1 Parent(s): f6f8725

Update README.md

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README.md +13 -1

README.md CHANGED Viewed

@@ -11,7 +11,7 @@ tags:
 - distillation
 - math
 ---
 ![Distil gpt oss logo](https://cdn-uploads.huggingface.co/production/uploads/6710ba6af1279fe0dfe33afe/RxoOFH7vZmoyyKOUlB_oX.png)
 # What it is
@@ -45,6 +45,18 @@ So the sequence starts:
 As you can see, you set the reasoning effort via the system prompt. We recommend going **2** lines down and only then putting "Reasoning effort: [low,medium,high]. For your information that output was generated by our model.
 # Additional information
 The model was trained using unsloth, using a mix of private datasets and public datasets.

 - distillation
 - math
 ---
+This is the bf16 safetensors variant
 ![Distil gpt oss logo](https://cdn-uploads.huggingface.co/production/uploads/6710ba6af1279fe0dfe33afe/RxoOFH7vZmoyyKOUlB_oX.png)
 # What it is
 As you can see, you set the reasoning effort via the system prompt. We recommend going **2** lines down and only then putting "Reasoning effort: [low,medium,high]. For your information that output was generated by our model.
+# Examples
+1) "Is a banana an animal?" Reasoning was set to **high**.
+![Is a banana an animal?](https://cdn-uploads.huggingface.co/production/uploads/6710ba6af1279fe0dfe33afe/f1N8knMusup4dugZ2WREB.png)
+2) "Write an HTML website about yourself" Reasoning was set to **medium**.
+![Write an HTML website about yourself](https://cdn-uploads.huggingface.co/production/uploads/6710ba6af1279fe0dfe33afe/azInLvZ1KGpT5DXT2zCyV.png)
+3) "translate this to chinese: Hello! I am ChatGPT. A large language model by OpenAi." Reasoning was set to **low**.
+![translate this to chinese: Hello! I am ChatGPT. A large language model by OpenAi.](https://cdn-uploads.huggingface.co/production/uploads/6710ba6af1279fe0dfe33afe/YH4Q0UY3aqeHRNhOgWv_V.png)
+As you can see, based on the reasoning effort of the model and your prompt, the model would think for a different amount of time.
+Keep in mind, these tests were done in LM Studio, GGUF q8_0 on a single consumer card (rtx 3080) where we got 95 - 80 Tokens/Second on 8192 context.
 # Additional information
 The model was trained using unsloth, using a mix of private datasets and public datasets.