| ---
|
| 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))
|
| ``` |
