Instructions to use ggbetz/qwen3-4b-think-s1-full-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ggbetz/qwen3-4b-think-s1-full-sft with Transformers:

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

pipe = pipeline("text-generation", model="ggbetz/qwen3-4b-think-s1-full-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ggbetz/qwen3-4b-think-s1-full-sft")
model = AutoModelForCausalLM.from_pretrained("ggbetz/qwen3-4b-think-s1-full-sft", 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 ggbetz/qwen3-4b-think-s1-full-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ggbetz/qwen3-4b-think-s1-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": "ggbetz/qwen3-4b-think-s1-full-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ggbetz/qwen3-4b-think-s1-full-sft

SGLang

How to use ggbetz/qwen3-4b-think-s1-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 "ggbetz/qwen3-4b-think-s1-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": "ggbetz/qwen3-4b-think-s1-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 "ggbetz/qwen3-4b-think-s1-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": "ggbetz/qwen3-4b-think-s1-full-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ggbetz/qwen3-4b-think-s1-full-sft with Docker Model Runner:
```
docker model run hf.co/ggbetz/qwen3-4b-think-s1-full-sft
```

modrill/qwen3-4b-think-s1-full-sft

Full-parameter supervised fine-tuning (SFT) of Qwen/Qwen3-4B-Base on the think_s1 curriculum stage (easy + medium code reasoning data). This checkpoint is Stage1 of the think curriculum (checkpoint-1094).

Related models

Think baseline full SFT: modrill/qwen3-4b-think-baseline-full-sft
Nothink baseline full SFT: modrill/qwen3-4b-nothink-baseline-full-sft

Training summary

Field	Value
Method	Full SFT (DeepSpeed ZeRO-2)
Dataset	think_s1 (easy + medium, 72,555 samples)
Chat template	qwen3
Thinking mode	enable_thinking=true
Cutoff length	16384
Packing	true (neat_packing)
Epochs	2
Global batch	64 (4 GPU × 4 × 4)
Learning rate	1e-5
LR schedule	cosine, warmup 10%
Train steps	1094
Final train loss	~0.57
Finished	2026-06-09

Eval (EvalScope, release_latest / AIME)

Benchmark	pass@1	Config
LiveCodeBench	36.06%	t=0.6, p=0.95, max_tokens=16384
AIME24	16.67%	same sampling, max_tokens=16384
AIME25	3.33%	same sampling, max_tokens=16384

Usage

HuggingFace Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "modrill/qwen3-4b-think-s1-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-s1-full-sft \
  --served-model-name think-s1 \
  --max-model-len 32768 \
  --port 8801

Inference tips

Use Qwen3 chat template with thinking enabled
Recommended eval max_tokens: 16384 (matches training cutoff)
Sampling: temperature=0.6, top_p=0.95, top_k=20

License

Apache 2.0, consistent with the Qwen3 base model license.

Downloads last month: 323

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for ggbetz/qwen3-4b-think-s1-full-sft

Base model

Qwen/Qwen3-4B-Base

Finetuned

(463)

this model