59 lines
2.1 KiB
Markdown
59 lines
2.1 KiB
Markdown
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---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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datasets:
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- openai/gsm8k
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- gsm8k
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- math
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- sft
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- qwen2.5
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---
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# Qwen2.5-0.5B-Instruct — GSM8k SFT
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Full supervised fine-tune of [`Qwen/Qwen2.5-0.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
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on the [GSM8k](https://huggingface.co/datasets/openai/gsm8k) training split (7,473 worked
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solutions), trained with [verl](https://github.com/volcengine/verl)'s `sft_trainer`.
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## Results
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| Metric | Value |
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|---|---|
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| GSM8k test accuracy (greedy, 1319 examples) | **36.2%** (477/1319) |
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## Training
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- Base model: `Qwen/Qwen2.5-0.5B-Instruct`
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- Method: full fine-tune (no LoRA), bf16 mixed precision
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- Data: GSM8k train, chat format (system + user); assistant target is the full worked solution ending in `#### N`
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- Epochs: 3 (348 steps); AdamW, lr 1e-5 cosine, warmup ratio 0.1, weight decay 0.1, grad clip 1.0
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- Max sequence length: 1024
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## Prompt format
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System prompt used during training; the model ends its answer with `#### <number>`:
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> You are a math problem solver. Think step by step. You MUST end your response with '#### <number>' where <number> is the final numerical answer (digits only, no units or markdown).
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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m = AutoModelForCausalLM.from_pretrained("OhhMoo/qwen05b-gsm8k-sft-instruct")
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tok = AutoTokenizer.from_pretrained("OhhMoo/qwen05b-gsm8k-sft-instruct")
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messages = [
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{"role": "system", "content": "You are a math problem solver. Think step by step. You MUST end your response with '#### <number>' where <number> is the final numerical answer (digits only, no units or markdown)."},
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{"role": "user", "content": "Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May? Let's think step by step."},
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]
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ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
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out = m.generate(ids, max_new_tokens=512, do_sample=False)
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print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))
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```
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