---
license: mit

language:
- en
pipeline_tag: text-generation
tags:
- nlp
- code
inference:
  parameters:
    temperature: 0.0
widget:
  - messages:
      - role: user
        content: How to express n-th root of the determinant of a semidefinite matrix in cvx?
---
# cvx-coder
[Github](https://github.com/jackfsuia/cvx-coder) | [Modelscope](https://www.modelscope.cn/models/tommy1235/cvx-coder)

## Introduction

cvx-coder aims to improve the [Matlab CVX](https://cvxr.com/cvx) code ability and QA ability of LLMs. It is a [phi-3 model](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) finetuned on a dataset consisting of CVX docs, codes, [forum conversations](https://ask.cvxr.com/) ( my cleaned version of them is at [CVX-forum-conversations](https://huggingface.co/datasets/tim1900/CVX-forum-conversations)).

## Quickstart
For one quick test, run the following:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
m_path="tim1900/cvx-coder"
model = AutoModelForCausalLM.from_pretrained(
    m_path, 
    device_map="auto", 
    torch_dtype="auto", 
    trust_remote_code=True, 
)
tokenizer = AutoTokenizer.from_pretrained(m_path)
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
)
generation_args = {
    "max_new_tokens": 2000,
    "return_full_text": False,
    "temperature": 0,
    "do_sample": False,
}
content='''my problem is not convex, can i use cvx? if not, what should i do, be specific.'''
messages = [
    {"role": "user", "content": content},
]
output = pipe(messages, **generation_args)
print(output[0]['generated_text'])
```
For the **chat mode** in web, run the following:
```python
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
m_path="tim1900/cvx-coder"
model = AutoModelForCausalLM.from_pretrained(
    m_path, 
    device_map="auto", 
    torch_dtype="auto", 
    trust_remote_code=True, 
)
tokenizer = AutoTokenizer.from_pretrained(m_path)
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
)
generation_args = {
    "max_new_tokens": 2000,
    "return_full_text": False,
    "temperature": 0,
    "do_sample": False,
}

def assistant_talk(message, history):
    message=[
        {"role": "user", "content": message},
        ]
    temp=[]
    for i in history:
        temp+=[{"role": "user", "content": i[0]},{"role": "assistant", "content": i[1]}]
        
    messages =temp  + message

    output = pipe(messages, **generation_args)
    return output[0]['generated_text']
gr.ChatInterface(assistant_talk).launch()
```