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Model: Lixiaojian123/qwen3-0.6b-sft
Source: Original Platform

README.md

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+---
+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.