language, license, base_model, library_name, pipeline_tag, tags, datasets

language	license	base_model	library_name	pipeline_tag	tags	datasets
zh en	apache-2.0	Qwen/Qwen3-0.6B-Base	transformers	text-generation	qwen3 qwen3-0.6b sft trl smoltalk2 instruction-tuned	HuggingFaceTB/smoltalk2

Qwen3-0.6B SFT on SmolTalk2

This model is a supervised fine-tuned version of Qwen/Qwen3-0.6B-Base. It was trained with TRL SFT on selected no-think splits from HuggingFaceTB/smoltalk2.

The chat template was copied from the Qwen3 Instruct tokenizer so the model can be used with the standard chat-message interface.

Training

• Base model: Qwen/Qwen3-0.6B-Base
• Method: full-parameter SFT
• Framework: TRL / Transformers
• Dataset: HuggingFaceTB/smoltalk2, config SFT
• Splits:
  • smoltalk_smollm3_smol_magpie_ultra_no_think
  • OpenHermes_2.5_no_think
  • tulu_3_sft_personas_instruction_following_no_think
  • smoltalk_smollm3_systemchats_30k_no_think
• Max length: 2048
• Steps: 5000
• Learning rate: 2e-5, cosine schedule, warmup ratio 0.03
• Effective batch size: 64
• Gradient clipping: 1.0

Evaluation

The table below compares the official Qwen3-0.6B Instruct model against the official Base model using lm-eval.

Task	Base	Instruct	Delta
ifeval	16.27%	43.62%	+27.36pp
truthfulqa_mc2	45.98%	42.84%	-3.15pp
mmlu	50.46%	40.26%	-10.20pp
arc_challenge	38.65%	34.13%	-4.52pp
hellaswag	53.67%	47.29%	-6.38pp
winogrande	58.56%	56.35%	-2.21pp
gsm8k_cot	55.50%	4.55%	-50.95pp

The locally trained SFT checkpoint used as the DPO reference scored as follows:

Task	SFT score
ifeval	39.19%
truthfulqa_mc2	46.52%
mmlu	49.35%
arc_challenge	37.03%
hellaswag	53.36%
winogrande	57.93%
gsm8k_cot	48.37%

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Lixiaojian123/qwen3-0.6b-sft"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [
    {"role": "user", "content": "用三句话解释什么是监督微调。"},
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

Limitations

This is a small 0.6B parameter model. It may hallucinate, make reasoning mistakes, and fail on complex instructions. The model was trained primarily on no-think instruction data, so it is not intended to expose long chain-of-thought traces.