79 lines
2.3 KiB
Markdown
79 lines
2.3 KiB
Markdown
|
---
|
||
|
|
library_name: transformers
|
||
|
|
tags:
|
||
|
|
- small-lm
|
||
|
|
- math
|
||
|
|
- reasoning
|
||
|
|
- slm
|
||
|
|
license: apache-2.0
|
||
|
|
datasets:
|
||
|
|
- openai/gsm8k
|
||
|
|
base_model:
|
||
|
|
- Qwen/Qwen3-0.6B
|
||
|
|
---
|
||
|
|
|
||
|
|
# Qwen3-0.6B-Math
|
||
|
|
|
||
|
|
This model is obtained by fine-tuning Qwen/Qwen3-0.6B on the [gsm8k](https://huggingface.co/datasets/openai/gsm8k) train split.
|
||
|
|
The model is used in the experiments described in https://bknyaz.github.io/blog/2026/meta-merge/.
|
||
|
|
Single A100 was used for fine-tuning and evaluation.
|
||
|
|
|
||
|
|
The following versions were used for train/eval:
|
||
|
|
|
||
|
|
- python >= 3.10
|
||
|
|
- torch : 2.9.0+cu128
|
||
|
|
- lm_eval : 0.4.9.1
|
||
|
|
- vllm : 0.11.1
|
||
|
|
- transformers : 4.57.6
|
||
|
|
- datasets : 3.2.0
|
||
|
|
- numpy : 2.2.6
|
||
|
|
|
||
|
|
## Training
|
||
|
|
|
||
|
|
The [TRL](https://github.com/huggingface/trl) library was used with SFT/full-rank options:
|
||
|
|
|
||
|
|
```bash
|
||
|
|
python trl/scripts/sft.py --model_name_or_path Qwen/Qwen3-0.6B --dataset_name openai/gsm8k --dataset_config main --learning_rate 2e-5 \
|
||
|
|
--num_train_epochs 1 --per_device_train_batch_size 2 --gradient_accumulation_steps 8 --gradient_checkpointing --eos_token '<|im_end|>' --eval_strategy steps \
|
||
|
|
--eval_steps 100 --completion_only_loss True --report_to wandb --output_dir /path/to/the/finetuned/model
|
||
|
|
```
|
||
|
|
|
||
|
|
This is by far not the most compute and performance efficient fine-tuning, but it could be a good baseline.
|
||
|
|
|
||
|
|
The dataset was preprocessed to the conversational format:
|
||
|
|
|
||
|
|
```python
|
||
|
|
# trl/scripts/sft.py
|
||
|
|
|
||
|
|
dataset = load_dataset(...)
|
||
|
|
|
||
|
|
def preprocess_function(example):
|
||
|
|
return {
|
||
|
|
"prompt": [{"role": "user", "content": example["question"]}],
|
||
|
|
"completion": [
|
||
|
|
{"role": "assistant", "content": example['answer']}
|
||
|
|
],
|
||
|
|
}
|
||
|
|
|
||
|
|
dataset = dataset.map(preprocess_function)
|
||
|
|
```
|
||
|
|
|
||
|
|
## Evaluation
|
||
|
|
|
||
|
|
Evaluation was done with lm_eval on the test split of gsm8k:
|
||
|
|
|
||
|
|
```bash
|
||
|
|
python -m lm_eval --model vllm --model_args pretrained=${model},tensor_parallel_size=1,dtype=auto,gpu_memory_utilization=0.9,data_parallel_size=1 \
|
||
|
|
--tasks gsm8k --batch_size 1 --apply_chat_template=True --confirm_run_unsafe_code --trust_remote_code
|
||
|
|
```
|
||
|
|
|
||
|
|
### Results
|
||
|
|
|
||
|
|
| Model | gsm8k|
|
||
|
|
|-----------------------|------|
|
||
|
|
| Qwen3-0.6B | 21.0 |
|
||
|
|
| Qwen3-0.6B-Math | 46.3 |
|
||
|
|
|
||
|
|
## License
|
||
|
|
|
||
|
|
Please refer to the license of the original model [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) and dataset [gsm8k](https://huggingface.co/datasets/openai/gsm8k).
|