---
license: apache-2.0
datasets:
- AI-MO/NuminaMath-TIR
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
metrics:
- accuracy
base_model:
- Qwen/Qwen2.5-0.5B-Instruct
---
# NeuroCoder Qwen2.5-0.5B-Instruct-MemoryR

## Overview

This is the Hugging Face checkpoint of **Qwen2.5-0.5B-Instruct-MemoryR**, a memory-augmented RL-tuned model based on Qwen2.5. 

The model is introduced and analyzed in our paper: https://arxiv.org/abs/2504.02273

## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("neurocoder/Qwen2.5-0.5B-Instruct-MemoryR")
model = AutoModelForCausalLM.from_pretrained("neurocoder/Qwen2.5-0.5B-Instruct-MemoryR")

# Example input
prompt = "What is the capital of France?"
inputs = tokenizer(prompt, return_tensors="pt")

# Generate output
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```