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 20, 2025

Commit

39010c8

verified ·

1 Parent(s): dbe8be3

Update README.md

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@@ -21,6 +21,34 @@ DistilGPT-OSS-qwen3-4B is a Qwen3 4B-2507 thinking fine tune, it supports up to
 Keep in mind, this is a community project and we are NOT related to qwen by Alibaba nor GPT-OSS by OpenAi.
 # Format
 This is the chat format of this model (you can also check the Jinja template file in "Files and versions"):
 ```
@@ -59,4 +87,4 @@ Keep in mind, these tests were done in LM Studio, GGUF q8_0 on a single consumer
 # Additional information
-The model was trained using unsloth, using a mix of private datasets and public datasets.

 Keep in mind, this is a community project and we are NOT related to qwen by Alibaba nor GPT-OSS by OpenAi.
+# Use cases & benefits
+Benefits of using this model over standard qwen3 4b thinking:
+- You decide how much it would think (low, medium, high)
+- completely different style of answers (more similar to ChatGPT)
+- Produces less emoji (qwen3 4b uses quite a lot in its responses which some may not like)
+- Less censored/limiting than qwen3 4b
+DistilGPT-OSS-qwen3-4B should be used for the following:
+- Local on device efficient assistance
+- Code generation
+- Summary generation
+- General use
+Or anything else
+❌⚠️ It should ABSOLUTELY **not** be used for:
+- High-risk workspaces
+- Medical questions
+- Anything high risk which requires 1:1 accuracy.
+It is a small model thus general knowledge is limited to its size.
 # Format
 This is the chat format of this model (you can also check the Jinja template file in "Files and versions"):
 ```
 # Additional information
+The model was trained using unsloth, using a mix of private datasets and public datasets.