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---
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license: apache-2.0
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datasets:
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- AI-MO/NuminaMath-TIR
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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metrics:
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- accuracy
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base_model:
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- Qwen/Qwen2.5-0.5B-Instruct
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---
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# NeuroCoder Qwen2.5-0.5B-Instruct-MemoryR
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## Overview
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This is the Hugging Face checkpoint of **Qwen2.5-0.5B-Instruct-MemoryR**, a memory-augmented RL-tuned model based on Qwen2.5.
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The model is introduced and analyzed in our paper: https://arxiv.org/abs/2504.02273
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Load tokenizer and model
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tokenizer = AutoTokenizer.from_pretrained("neurocoder/Qwen2.5-0.5B-Instruct-MemoryR")
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model = AutoModelForCausalLM.from_pretrained("neurocoder/Qwen2.5-0.5B-Instruct-MemoryR")
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# Example input
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prompt = "What is the capital of France?"
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inputs = tokenizer(prompt, return_tensors="pt")
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# Generate output
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outputs = model.generate(**inputs, max_new_tokens=50)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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``` |
