tim1900_-_cvx-coder-gguf

Quantization made by Richard Erkhov.

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cvx-coder - GGUF

• Model creator: https://huggingface.co/tim1900/
• Original model: https://huggingface.co/tim1900/cvx-coder/

Original model description:

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?

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cvx-coder

Github | Modelscope

Introduction

cvx-coder aims to improve the Matlab CVX code ability and QA ability of LLMs. It is a phi-3 model finetuned on a dataset consisting of CVX docs, codes, forum conversations ( my cleaned version of them is at CVX-forum-conversations).

Quickstart

For one quick test, run the following:

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:

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()