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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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- en |
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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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``` |
