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Model: pymlex/qwen3-4b-gsm8k Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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license: gpl-3.0
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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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metrics:
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- exact_match
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base_model:
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- MegaScience/Qwen3-4B-MegaScience
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pipeline_tag: text-generation
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---
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# Qwen3-4B-MegaScience GSM8K fine-tune
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## Overview
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`MegaScience/Qwen3-4B-MegaScience` is a 4B Qwen3 checkpoint. We fine-tuned it on GSM8K, a grade-school math dataset with calculation annotations. The model learns to keep the reasoning trace in `<think>` and the final result in `<answer>`. A sample training target looks like this:
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```text
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<think>
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She sells 16 - 3 - 4 = 9 eggs each day.
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She makes 9 * 2 = 18.
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</think>
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<answer>
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18
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</answer>
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````
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## Dataset
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The training data comes from the official `openai/gsm8k` `main` split. Each example contains a question and a worked solution. The final answer is taken from the `####` line and moved into the `<answer>` block during formatting. The split is `95/5` from the official train split:
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* Train: `7,099` samples
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* Validation: `374` samples
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* Test: `1,319` samples
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The maximum sequence length is `768`. The token-length distribution:
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## Training
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Fine-tuning was performed with LoRA and supervised fine-tuning on a single RTX 5090.
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Training settings:
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* GPU: NVIDIA GeForce RTX 5090
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* VRAM: 31.36 GB
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* CPU: Ryzen 9 9950X
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* RAM: 62 GB
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Training configuration:
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* max sequence length: `768`
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* batch size: `4`
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* gradient accumulation: `8`
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* epochs: `1`
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* learning rate: `2e-4`
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* warmup steps: `20`
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* scheduler: cosine
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* optimiser: `adamw_torch`
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* LoRA rank: `16`
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* LoRA alpha: `32`
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* LoRA dropout: `0.05`
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## Loss and validation curves
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Training loss and validation loss move down during the run and then settle near a stable plateau:
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A logarithmic view:
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Training doesn’t show any improvement in the accuracy metric, which demonstrates the inefficiency of training on task-solution pairs while trying to give the model common sense.
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## Evaluation
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The final test evaluation is run with greedy decoding on the full GSM8K test split. Results:
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| | Loss | Perplexity |
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|---|---|---|
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| Validation | 0.3690 | 1.4463 |
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| Test | 0.3441 | 1.4107 |
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Metrics:
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* Validation exact match on 100 examples: `0.2100`
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* Test exact match: `0.0955`
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## Inference
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Use these two cells for inference.
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```python
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import re
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from peft import PeftModel
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base_model_id = "MegaScience/Qwen3-4B-MegaScience"
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adapter_id = "pymlex/qwen3-4b-gsm8k"
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tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.padding_side = "left"
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base_model = AutoModelForCausalLM.from_pretrained(
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base_model_id,
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device_map="auto",
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torch_dtype=torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float16,
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trust_remote_code=True,
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)
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model = PeftModel.from_pretrained(base_model, adapter_id)
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model.eval()
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```
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```python
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SYSTEM_PROMPT = (
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"You solve grade-school math problems. "
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"Put the reasoning in <think>...</think>. "
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"Put only the final result as a single number in <answer>...</answer>."
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)
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def build_prompt(question):
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": question.strip()},
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]
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return tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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def solve_question(model, tokenizer, question, max_new_tokens=512):
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prompt = build_prompt(question)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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prompt_len = inputs["input_ids"].shape[-1]
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end_answer_id = tokenizer.convert_tokens_to_ids("</answer>")
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eos_id = tokenizer.eos_token_id
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with torch.inference_mode():
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output = model.generate(
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**inputs,
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max_new_tokens=max_new_tokens,
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do_sample=False,
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eos_token_id=[eos_id, end_answer_id],
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pad_token_id=tokenizer.pad_token_id,
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)
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new_tokens = output[0][prompt_len:]
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text = tokenizer.decode(new_tokens, skip_special_tokens=False)
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if "</answer>" in text:
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text = text.split("</answer>")[0] + "</answer>"
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return text.strip()
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sample_question = (
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"Janet’s ducks lay 16 eggs per day. She eats three for breakfast every morning and "
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"bakes muffins for her friends every day with four. She sells the remainder at the "
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"farmers' market daily for $2 per fresh duck egg. How much in dollars does she make "
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"every day at the farmers' market?"
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)
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print(solve_question(model, tokenizer, sample_question))
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```
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