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qwen3-1.7b-rlvr-gsm8k
tocsa/qwen3-1.7b-rlvr-gsm8k
qwen3-1.7b-rlvr-gsm8k is a machine learning model from tocsa. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
Fine-tuned from Qwen/Qwen3-1.7B using Reinforcement Learning from Verifiable Rewards (RLVR) on the GSM8K grade school math dataset.
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At a glance
- License
- apache-2.0
- Model type
- qwen3
- Access
- Public
- Created
- Jun 11, 2026
- Updated
- Jun 11, 2026
- SHA
- 64c6aeec
Base models
Datasets
Model card
From the Hugging Face model README
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))
- Type
- qwen3
- License
- apache-2.0
- Languages
- en
- Created
- Jun 11, 2026
- Updated
- Jun 11, 2026