update readme

README.md

@@ -5,7 +5,66 @@ library_name: peft
 
 ---
 
-### Model Sources
-<!-- Provide the basic links for the model. -->
-- **Repository:** https://github.com/pku-liang/OriGen
-- **Paper:** https://arxiv.org/abs/2407.16237
+# OriGen: Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection
+
+### Introduction
+OriGen is a fine-tuned lora model designed for Verilog code generation. It is trained on top of DeepSeek Coder 7B using datasets generated from code-to-code augmentation and self-reflection.
+
+
+**Repository:** [pku-liang/OriGen](https://github.com/pku-liang/OriGen)
+
+### Evaluation Results
+<img src="figures/evaluation.png" alt="evaluation" width="1000"/>
+
+### Quick Start
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+import torch
+from peft import PeftModel
+
+model_name = "deepseek-ai/deepseek-coder-7b-instruct-v1.5"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    low_cpu_mem_usage=True,
+    torch_dtype=torch.float16,
+    attn_implementation="flash_attention_2",
+    device_map="auto",
+)
+
+model = PeftModel.from_pretrained(model, model_id="henryen/OriGen")
+model.eval()
+
+streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+prompt = "### Instruction: Please act as a professional Verilog designer. and provide Verilog code based on the given instruction. Generate a concise Verilog module for a 8 bit full adder, don't include any unnecessary code.\n### Response: "
+
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+
+outputs = model.generate(
+    **inputs, 
+    max_new_tokens=1000, 
+    do_sample=False, 
+    temperature=0, 
+    eos_token_id=tokenizer.eos_token_id,
+    pad_token_id=tokenizer.pad_token_id,
+    streamer=streamer
+)
+```
+
+### Paper
+**Arxiv:** https://arxiv.org/abs/2407.16237
+
+Please cite our paper if you use this model.
+
+```
+@article{2024origen,
+  title={OriGen: Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection},
+  author={Cui, Fan and Yin, Chenyang and Zhou, Kexing and Xiao, Youwei and Sun, Guangyu and Xu, Qiang and Guo, Qipeng and Song, Demin and Lin, Dahua and Zhang, Xingcheng and others},
+  journal={arXiv preprint arXiv:2407.16237},
+  year={2024}
+}
+```