Instructions to use wyt2000/CodeV-SVA-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wyt2000/CodeV-SVA-14B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="wyt2000/CodeV-SVA-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wyt2000/CodeV-SVA-14B")
model = AutoModelForCausalLM.from_pretrained("wyt2000/CodeV-SVA-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wyt2000/CodeV-SVA-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wyt2000/CodeV-SVA-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wyt2000/CodeV-SVA-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wyt2000/CodeV-SVA-14B

SGLang

How to use wyt2000/CodeV-SVA-14B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "wyt2000/CodeV-SVA-14B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wyt2000/CodeV-SVA-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "wyt2000/CodeV-SVA-14B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wyt2000/CodeV-SVA-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wyt2000/CodeV-SVA-14B with Docker Model Runner:
```
docker model run hf.co/wyt2000/CodeV-SVA-14B
```

wyt2000 commited on Nov 25, 2025

Commit

211ca46

verified ·

1 Parent(s): 52d40ac

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@@ -1,47 +1,50 @@
-<!-- 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. -->
-# sva-seq-160K-with-R1-more-than-V3-no-eventually-fixed-qwen3-14B-1112
-This model is a fine-tuned version of [/nfs_global/models/Qwen3-14B/](https://huggingface.co//nfs_global/models/Qwen3-14B/) 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.
-## Model description
-More information needed
-## Intended uses & limitations
-More information needed
-## Training and evaluation data
-More information needed
-## Training procedure
-### Training hyperparameters
-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
-### Training results
-### Framework versions
-- Transformers 4.57.1
-- Pytorch 2.9.0+cu128
-- Datasets 4.0.0
-- Tokenizers 0.22.1

+---
+language:
+- en
+base_model: Qwen/Qwen3-14B
+tags:
+- chat
+library_name: transformers
+license: apache-2.0
+---
+# CodeV-SVA: Training Specialized LLMs for Hardware Assertion Generation via RTL-Grounded Bidirectional Data Synthesis
+<div align="center">
+  <a href="https://huggingface.co/wyt2000/CodeV-SVA-14B"><img src="https://img.shields.io/static/v1?label=Model&message=HuggingFace&color=yellow"></a> &ensp;
+</div>
+## Introduction
+We introduce CodeV-SVA, a family of large language models designed to translate natural-language verification properties into SystemVerilog Assertions (SVAs).
+Open-Source Plan:
+- Model ✓
+- Paper
+- Dataset
+- Evaluation code
+- Data synthesis and training code
+## Models
+| Model | Download |
+| -------- | -------- |
+|    CodeV-SVA-8B     |   [🤗HuggingFace](https://huggingface.co/wyt2000/CodeV-SVA-8B)    |
+|    CodeV-SVA-14B     |   [🤗HuggingFace](https://huggingface.co/wyt2000/CodeV-SVA-14B)    |
+## Usage
+See [`inference.py`](https://huggingface.co/wyt2000/SVACoder-14B/blob/main/inference.py).
+## Citation
+```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}},
+}
+```