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

```python
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.