---
language:
- en
metrics:
- code_eval
base_model:
- deepseek-ai/deepseek-coder-6.7b-base
pipeline_tag: text-generation
library_name: transformers
tags:
- code
---
# AssertSolver

<!-- Provide a quick summary of what the model is/does. -->


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Finetuned from model:** deepseek-ai/deepseek-coder-6.7b-base

### Model Sources

<!-- Provide the basic links for the model. -->

- **Paper:** Insights from Rights and Wrongs: A Large Language Model for Solving Assertion Failures in RTL Design


## How to Get Started with the Model

Use the code below to get started with the model.

```
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "1412312anonymous/AssertSolver"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
prompt = "Tell me how to fix the bugs inside: `always(*) // Pretend that this * should be rst`"

messages = [{"role": "user", "content": prompt}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(
    inputs,
    max_new_tokens=512,
    do_sample=False,
    top_k=50,
    top_p=0.95,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0][len(inputs[0]) :], skip_special_tokens=True))
```