library_name, license, datasets, language, metrics, base_model, pipeline_tag

library_name	license	datasets	language	metrics	base_model	pipeline_tag
transformers	gpl-3.0	openai/gsm8k	en	exact_match	MegaScience/Qwen3-4B-MegaScience	text-generation

Qwen3-4B-MegaScience GSM8K fine-tune

Overview

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:

<think>
She sells 16 - 3 - 4 = 9 eggs each day.
She makes 9 * 2 = 18.
</think>
<answer>
18
</answer>

Dataset

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:

• Train: 7,099 samples
• Validation: 374 samples
• Test: 1,319 samples

The maximum sequence length is 768. The token-length distribution:

Training

Fine-tuning was performed with LoRA and supervised fine-tuning on a single RTX 5090.

Training settings:

• GPU: NVIDIA GeForce RTX 5090
• VRAM: 31.36 GB
• CPU: Ryzen 9 9950X
• RAM: 62 GB

Training configuration:

• max sequence length: 768
• batch size: 4
• gradient accumulation: 8
• epochs: 1
• learning rate: 2e-4
• warmup steps: 20
• scheduler: cosine
• optimiser: adamw_torch
• LoRA rank: 16
• LoRA alpha: 32
• LoRA dropout: 0.05

Loss and validation curves

Training loss and validation loss move down during the run and then settle near a stable plateau:

A logarithmic view:

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.

Evaluation

The final test evaluation is run with greedy decoding on the full GSM8K test split. Results:

	Loss	Perplexity
Validation	0.3690	1.4463
Test	0.3441	1.4107

Metrics:

• Validation exact match on 100 examples: 0.2100
• Test exact match: 0.0955

Inference

Use these two cells for inference.

import re
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model_id = "MegaScience/Qwen3-4B-MegaScience"
adapter_id = "pymlex/qwen3-4b-gsm8k"

tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left"

base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float16,
    trust_remote_code=True,
)

model = PeftModel.from_pretrained(base_model, adapter_id)
model.eval()

SYSTEM_PROMPT = (
    "You solve grade-school math problems. "
    "Put the reasoning in <think>...</think>. "
    "Put only the final result as a single number in <answer>...</answer>."
)

def build_prompt(question):
    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": question.strip()},
    ]
    return tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True,
    )

def solve_question(model, tokenizer, question, max_new_tokens=512):
    prompt = build_prompt(question)
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    prompt_len = inputs["input_ids"].shape[-1]

    end_answer_id = tokenizer.convert_tokens_to_ids("</answer>")
    eos_id = tokenizer.eos_token_id

    with torch.inference_mode():
        output = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            do_sample=False,
            eos_token_id=[eos_id, end_answer_id],
            pad_token_id=tokenizer.pad_token_id,
        )

    new_tokens = output[0][prompt_len:]
    text = tokenizer.decode(new_tokens, skip_special_tokens=False)

    if "</answer>" in text:
        text = text.split("</answer>")[0] + "</answer>"

    return text.strip()

sample_question = (
    "Janet’s ducks lay 16 eggs per day. She eats three for breakfast every morning and "
    "bakes muffins for her friends every day with four. She sells the remainder at the "
    "farmers' market daily for $2 per fresh duck egg. How much in dollars does she make "
    "every day at the farmers' market?"
)

print(solve_question(model, tokenizer, sample_question))