Instructions to use mlkro/Qwen3-4B-PT-SynthDolly-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlkro/Qwen3-4B-PT-SynthDolly-1B with Transformers:

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

pipe = pipeline("text-generation", model="mlkro/Qwen3-4B-PT-SynthDolly-1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mlkro/Qwen3-4B-PT-SynthDolly-1B")
model = AutoModelForCausalLM.from_pretrained("mlkro/Qwen3-4B-PT-SynthDolly-1B", 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 mlkro/Qwen3-4B-PT-SynthDolly-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlkro/Qwen3-4B-PT-SynthDolly-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlkro/Qwen3-4B-PT-SynthDolly-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mlkro/Qwen3-4B-PT-SynthDolly-1B

SGLang

How to use mlkro/Qwen3-4B-PT-SynthDolly-1B 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 "mlkro/Qwen3-4B-PT-SynthDolly-1B" \
    --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": "mlkro/Qwen3-4B-PT-SynthDolly-1B",
		"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 "mlkro/Qwen3-4B-PT-SynthDolly-1B" \
        --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": "mlkro/Qwen3-4B-PT-SynthDolly-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use mlkro/Qwen3-4B-PT-SynthDolly-1B with Docker Model Runner:
```
docker model run hf.co/mlkro/Qwen3-4B-PT-SynthDolly-1B
```

mlkro commited on Dec 29, 2025

Commit

9b7192a

verified ·

1 Parent(s): eca280a

(Trained with Unsloth)

Browse files

Files changed (1) hide show

model-00002-of-00002.safetensors +1 -1

model-00002-of-00002.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c66532e7d536618490fee9571a552e1ca255f3003b65183212bb232416dc307e
 size 3077766632

 version https://git-lfs.github.com/spec/v1
+oid sha256:f7570bde80f2e4ea0f925800d0291ba85e27f2533850c25114a2e24b8e683dbf
 size 3077766632