README.md

---
library_name: transformers
datasets:
- glaiveai/glaive-code-assistant
language:
- en
base_model:
- mistralai/Mistral-7B-v0.1
license: apache-2.0
---

# Model Card for Model ID

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


## Model Summary
The goal of this model is to improve the quality and efficiency of code generation from natural language prompts, particularly for Python, since this is the programming language I use most often. Many LLMs produce code that is outdated, inefficient, and bugged. Creating a custom LLM that is able to produce efficient and quality code allows the user to reduce the amount of time it takes to write code and more quickly troubleshoot bugged code. Current models may inadvertently introduce vulnerabilities or generate code that does not adhere to current norms due to the training code data occasionally lacking the safety or output aligned with human coding preferences ([Jiang et al., 2024](https://arxiv.org/html/2406.00515v1)). Additionally, current models are frequently trained on large datasets that encompass a wide range of programming languages, giving the model a roughly equal amount of training time on many languages, which may affect performance on more popular languages ([Jiang et al., 2024](https://arxiv.org/html/2406.00515v1)). To combat this, I selected a model with 7 billion parameters that had a relatively strong performance at baseline on solving code tasks and trained the model on a large code generation dataset (~136,000 rows) that was ~60% Python code. I utilized a Program of Thought (PoT) prompting approach and LoRA training method to create an updated model. Finally, I compared the benchmark performances of MBPP, HumanEval, and MMLU on the updated model to the baseline model. The updated model had little improvement from the base model. For the tested benchmarks, MBPP rose from 37.6% to 40.2% on the first pass, and HumanEval first-pass accuracy dropped from 0.6% to 0%; however, the code appeared to have better format than the base model, and MMLU stayed about the same, with 59.6% at baseline and 59.1% after training.

### Model Description

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

- **Developed by:** Alden Swain
- **Model type:** Text Generation
- **Language(s) (NLP):** English
- **License:** Apache 2.0

### Model Sources

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- **Repository:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1}{mistralai/Mistral-7B-v0.1)
- **Paper:** [Mistral-7B](https://arxiv.org/abs/2310.06825)

## Uses

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### Direct Use

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[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

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The base model was trained on the Glaive code assistant dataset from Glaive AI. This dataset contains ~140,000 code problems and solutions. The problems are formatted in a natural language process, such as you would expect a human user to query. Approximately 60% of the data uses the Python language. The dataset only contains a test split, and this was split into a training and validation set. For reproducibility, the data was shuffled after loading in using data.sample() with only the hyperparameters frac=1 and random_state=42 used. I split the data approximately 90-10, with 122,000 rows in the train set and the remainder in the validation set.

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
A Program of Thought (PoT) prompting approach was used to modify the training data. PoT specifically focuses on generating structured, step-by-step reasoning in a more formalized and executable format than chain of thought. With PoT, the model generates a sequence of logical steps that resemble pseudo-code, which can then be translated into actual code. PoT should allow structured reasoning for code generation, should be able to debug broken code, and should provide a step-by-step breakdown of the plan ([Chen et al., 2023](https://arxiv.org/abs/2211.12588)).

PoT was implemented by creating a new column in the dataset that initiated a “plan” for the model to follow. The plan was a 4-step process: analyze the question from the train column, think step by step and plan an efficient solution before writing the code, consider any necessary programming constructs or tools, and explain the approach with well-organized and documented code. The answer to the question from the dataset was appended after the plan so there was a single text block for training. Lastly, before training started, the pandas dataframes (train and validation) were converted into Hugging Face Dataset objects, and the tokenizer was applied to the newly created column for each row.


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

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#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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