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@@ -8,4 +8,28 @@ 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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  - 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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+
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+ ## Overview
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+
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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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+
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+ The model is introduced and analyzed in our paper: https://arxiv.org/abs/2504.02273
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+
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+ ## Usage
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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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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+
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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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+
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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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+ ```