tocsa/qwen3-1.7b-rlvr-gsm8k

Qwen3-1.7B RLVR — GSM8K Math Reasoning

Fine-tuned from Qwen/Qwen3-1.7B using Reinforcement Learning from Verifiable Rewards (RLVR) on the GSM8K grade school math dataset.

Training Details

Base model	Qwen/Qwen3-1.7B
Technique	RLVR with LoRA (rank 32, alpha 64)
Dataset	GSM8K train split, 200 samples
Epochs	2
Learning rate	1e-5
Batch size	128
Training platform	Amazon SageMaker Serverless Model Customization

Intended Use

This model is trained to solve grade school math word problems with step-by-step reasoning. It is intended for educational and research purposes, particularly as a demonstration of RLVR fine-tuning on a small dataset.

Primary users: Workshop participants and developers exploring RLVR on SageMaker.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("tocsa/qwen3-1.7b-rlvr-gsm8k")
tokenizer = AutoTokenizer.from_pretrained("tocsa/qwen3-1.7b-rlvr-gsm8k")

prompt = "Janet's ducks lay 16 eggs per day. She eats 3 for breakfast and bakes 4 into muffins. She sells the rest for $2 each. How much does she make per day?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))