Instructions to use modrill/qwen3-4b-think-baseline-full-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use modrill/qwen3-4b-think-baseline-full-sft with Transformers:

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

pipe = pipeline("text-generation", model="modrill/qwen3-4b-think-baseline-full-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("modrill/qwen3-4b-think-baseline-full-sft")
model = AutoModelForMultimodalLM.from_pretrained("modrill/qwen3-4b-think-baseline-full-sft")
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 modrill/qwen3-4b-think-baseline-full-sft with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/modrill/qwen3-4b-think-baseline-full-sft

SGLang

How to use modrill/qwen3-4b-think-baseline-full-sft 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 "modrill/qwen3-4b-think-baseline-full-sft" \
    --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": "modrill/qwen3-4b-think-baseline-full-sft",
		"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 "modrill/qwen3-4b-think-baseline-full-sft" \
        --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": "modrill/qwen3-4b-think-baseline-full-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use modrill/qwen3-4b-think-baseline-full-sft with Docker Model Runner:
```
docker model run hf.co/modrill/qwen3-4b-think-baseline-full-sft
```

Qwen3-4B Code SFT - Think Baseline (Full SFT)

Full-parameter supervised fine-tuning (not LoRA) of Qwen/Qwen3-4B-Base on the think_all dataset with thinking mode enabled.

This repo contains native full fine-tuned weights (single model.safetensors, ~7.5 GB). For LoRA adapters merged into base weights, see modrill/qwen3-4b-think-baseline-lora-sft.

Model Details

Base model: Qwen/Qwen3-4B-Base
Fine-tuning: Full SFT (DeepSpeed ZeRO-3), finetuning_type: full
Dataset: think_all
Mode: Think (enable_thinking=true)
Training cutoff length: 24576 tokens
Epochs: 2
Learning rate: 2e-5
Train loss: ~0.67
Finished: 2026-06-08

Usage

HuggingFace Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "modrill/qwen3-4b-think-baseline-full-sft"
tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id, trust_remote_code=True, torch_dtype="auto", device_map="auto"
)

vLLM

python -m vllm.entrypoints.openai.api_server \
  --model modrill/qwen3-4b-think-baseline-full-sft \
  --served-model-name think-baseline-full \
  --port 8801

Inference Tips

Set enable_thinking=true in the chat template
Recommended max_tokens: 24576

License

Apache 2.0, consistent with the Qwen3 base model license.

Downloads last month: 2

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for modrill/qwen3-4b-think-baseline-full-sft

Base model

Qwen/Qwen3-4B-Base

Finetuned

(305)

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