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@@ -12,14 +12,14 @@ base_model:
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  - Qwen/Qwen2.5-Coder-1.5B-Instruct
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  ---
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- # VeriReason-Qwen2.5-1.5B-grpo-small
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  For implementation details, visit our GitHub repository: [VeriReason](https://github.com/NellyW8/VeriReason)
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  Check out our paper: [VeriReason: Reinforcement Learning with Testbench Feedback for Reasoning-Enhanced Verilog Generation](https://arxiv.org/abs/2505.11849)
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  ## Update Log
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- 2025.05.17: Initial release of VeriReason-Qwen2.5-1.5B-Verilog-RTL-GRPO-reasoning-tb
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  ## Project Description
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@@ -42,7 +42,7 @@ You can use the model with the transformers library:
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  import torch
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  from transformers import AutoTokenizer, AutoModelForCausalLM
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- model_name = "Nellyw888/VeriReason-Qwen2.5-1.5B-grpo-small"
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
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  model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
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  model.eval()
 
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  - Qwen/Qwen2.5-Coder-1.5B-Instruct
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  ---
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+ # VeriReason-Qwen2.5-1.5b-RTLCoder-Verilog-GRPO-reasoning-tb
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  For implementation details, visit our GitHub repository: [VeriReason](https://github.com/NellyW8/VeriReason)
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  Check out our paper: [VeriReason: Reinforcement Learning with Testbench Feedback for Reasoning-Enhanced Verilog Generation](https://arxiv.org/abs/2505.11849)
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  ## Update Log
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+ 2025.05.17: Initial release of VeriReason-Qwen2.5-1.5b-RTLCoder-Verilog-GRPO-reasoning-tb
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  ## Project Description
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  import torch
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  from transformers import AutoTokenizer, AutoModelForCausalLM
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+ model_name = "Nellyw888/VeriReason-Qwen2.5-1.5b-RTLCoder-Verilog-GRPO-reasoning-tb"
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
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  model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
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  model.eval()