Mirror Model Card

Summary

Mirror is a fine-tuned large language model built on Mistral, optimized for code generation, debugging, and structured technical assistance. It has been trained on the GPT CodeFeedback dataset, enhancing its ability to provide precise, context-aware programming suggestions. While not a state-of-the-art model, Mirror demonstrates strong code understanding, refactoring capabilities, and instruction-following behavior.

The model is fine-tuned using LoRA with a focus on efficient inference and is designed to assist developers in writing clean, optimized, and well-structured code.

Mirror is available in different configurations to support various deployment environments.

Model Overview

Mirror is a causal language model based on Mistral, trained using instruction tuning on a dataset designed to enhance code review, debugging, and structured programming responses. The model is intended for:

Code generation across multiple programming languages.
Code optimization and refactoring suggestions.
Explaining and debugging errors.
Providing structured, detailed coding assistance.

LangChain Usage

For applications using LangChain, set return_full_text=True to ensure the full response is returned.

from transformers import pipeline
from langchain import PromptTemplate, LLMChain
from langchain.llms import HuggingFacePipeline

generate_code = pipeline(model="your-huggingface-username/Mirror",
                         torch_dtype=torch.bfloat16,
                         trust_remote_code=True, 
                         device_map="auto", 
                         return_full_text=True)

prompt = PromptTemplate(
    input_variables=["instruction"],
    template="{instruction}")

hf_pipeline = HuggingFacePipeline(pipeline=generate_code)
llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt)

print(llm_chain.predict(instruction="Write a Python function to check if a number is prime."))

Known Limitations

While Mirror provides high-quality code suggestions, debugging assistance, and structured programming responses, it has the following limitations:

General conversation abilities are limited due to its specialization in coding-related tasks.
Mathematical reasoning and logical inference may be weaker than models designed for general problem-solving.
Complex multi-step reasoning in natural language might require fine-tuning on additional dialogue datasets.

Dataset Limitations

Mirror is fine-tuned on the GPT CodeFeedback dataset, which primarily focuses on code optimization and structured feedback. While it provides strong performance for technical queries, it may:

Reflect biases inherent in publicly available programming datasets.
Have limited knowledge of recent programming frameworks or libraries that emerged after its last fine-tuning session.
Exhibit hallucinations in open-ended prompts that lack specific instructions.

Future Development

Enhancing conversational abilities by fine-tuning on instruction-heavy dialogue datasets (e.g., OpenAssistant, Dolly).
Improving reasoning and debugging capabilities using reinforcement learning from developer interactions.
Reducing hallucinations in long-form responses through dataset refinements.

License

Mirror is released under the Apache License 2.0 and CC-BY-SA 4.0, allowing for both commercial and research usage.

Option 1: Apache License 2.0

Mirror is licensed under the Apache License, Version 2.0 (the "License");
you may not use this model except in compliance with the License.
You may obtain a copy of the License at:

📄 Apache 2.0 License

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

Option 2: Creative Commons Attribution-ShareAlike 4.0 (CC-BY-SA 4.0)

This model's outputs (such as generated text) and non-code content are licensed under CC-BY-SA 4.0.

Under this license:

You must give credit when using or sharing outputs.
You must share modifications under the same license.

📄 CC-BY-SA 4.0 License