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README.md

@@ -1,47 +1,54 @@
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+---
+language:
+- en
+base_model: Qwen/Qwen3-8B
+tags:
+- chat
+library_name: transformers
+license: apache-2.0
+---
 
-# sva-seq-160K-with-R1-more-than-V3-no-eventually-fixed-qwen3-8B-1112
+# CodeV-SVA: Training Specialized LLMs for Hardware Assertion Generation via RTL-Grounded Bidirectional Data Synthesis
 
-This model is a fine-tuned version of [/nfs_global/models/Qwen3-8B/](https://huggingface.co//nfs_global/models/Qwen3-8B/) on the codev-159K-pass-at-5-Qwen-7B-checked-no-all-pass-regenerated-R1-verification-pass-with-R1-more-than-V3-no-eventually-fixed-training dataset.
+<div align="center"> 
+  <a href="https://huggingface.co/wyt2000/CodeV-SVA-8B"><img src="https://img.shields.io/static/v1?label=Model&message=HuggingFace&color=yellow"></a> &ensp;
+</div>
+<br>
 
-## Model description
+## Introduction
 
-More information needed
+We introduce CodeV-SVA, a family of large language models designed to translate natural-language verification properties into SystemVerilog Assertions (SVAs).
 
-## Intended uses & limitations
+Open-Source Plan:
 
-More information needed
+- Model ✓
+- Paper
+- Dataset
+- Evaluation code
+- Data synthesis and training code
 
-## Training and evaluation data
+## Models
 
-More information needed
+<div align="center">
+  
+| Model | Download |
+| -------- | -------- |
+|    CodeV-SVA-8B     |   [🤗HuggingFace](https://huggingface.co/wyt2000/CodeV-SVA-8B)    |
+|    CodeV-SVA-14B     |   [🤗HuggingFace](https://huggingface.co/wyt2000/CodeV-SVA-14B)    |
 
-## Training procedure
+</div>
 
-### Training hyperparameters
+## Usage
 
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 1
-- eval_batch_size: 8
-- seed: 42
-- distributed_type: multi-GPU
-- num_devices: 8
-- gradient_accumulation_steps: 16
-- total_train_batch_size: 128
-- total_eval_batch_size: 64
-- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: cosine
-- num_epochs: 2.0
+See `inference.py`.
 
-### Training results
+## Citation
 
-
-
-### Framework versions
-
-- Transformers 4.57.1
-- Pytorch 2.9.0+cu128
-- Datasets 4.0.0
-- Tokenizers 0.22.1
+```latex
+@misc{CodeV-SVA,
+    title={CodeV-SVA: Training Specialized LLMs for Hardware Assertion Generation via RTL-Grounded Bidirectional Data Synthesis}, 
+    author={Yutong Wu and Chenrui Cao and Pengwei Jin and Di Huang and Rui Zhang and Xishan Zhang and Zidong Du and Qi Guo and Xing Hu},
+    year={2025},
+    howpublished={\url{https://huggingface.co/wyt2000/CodeV-SVA-14B}},
+}
+```