Model Card for Model ID: Arko007/my-awesome-code-assistant-v4
Model Details
Model Description
Developed by: Arko007

Funded by: Self-funded

Shared by: Arko007

Model type: Autoregressive language model for code (code assistant)

Language(s) (NLP): English, with support for various programming languages including Python, JavaScript, Java, and C++.

License: MIT License

Finetuned from model: bigcode/starcoder

Model Sources
Repository: https://huggingface.co/Arko007/my-awesome-code-assistant-v4 (A placeholder URL, as the repository is not public)

Paper [optional]: N/A

Demo [optional]: N/A

Uses
Direct Use
This model is intended for code-related tasks, including:

Code Completion: Generating the next few lines of code based on a prompt.

Code Generation: Creating functions, scripts, or small programs from natural language descriptions.

Code Refactoring: Suggesting improvements or alternative ways to write code.

Code Documentation: Generating docstrings and comments.

Downstream Use [optional]
This model can be used as a backend for integrated development environments (IDEs), developer tools, and educational platforms that require code assistance capabilities.

Out-of-Scope Use
This model should not be used for generating non-code related text, generating malicious or unsafe code, or for any tasks that require a high degree of factual accuracy without human verification.

Bias, Risks, and Limitations
Hallucinations: The model may generate code that looks plausible but is incorrect or contains bugs.

Security Vulnerabilities: The generated code may contain security flaws or unsafe practices. All generated code should be carefully reviewed by a human expert.

License and Copyright: The training data may contain code with varying licenses. Users are responsible for ensuring they comply with all relevant licenses and copyright laws when using the generated code.

Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. All generated code must be treated as a starting point and thoroughly reviewed, tested, and audited for correctness and security.

How to Get Started with the Model
Use the code below to get started with the model using the transformers library.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Arko007/my-awesome-code-assistant-v4"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "# Write a Python function to calculate the factorial of a number"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details
Training Data
This model was finetuned on a private dataset of curated open-source code snippets and documentation. The specific sources are not publicly disclosed, but it primarily consists of code from GitHub repositories with permissive licenses.

Training Procedure
Preprocessing: The training data was tokenized using the StarCoder tokenizer. Code comments were preserved to aid in documentation and explanation tasks.

Training Hyperparameters:

Training regime: Finetuning with a LoRA (Low-Rank Adaptation) approach.

Learning Rate: 2
times10 
−4
 

Batch Size: 4

Epochs: 3

Optimizer: AdamW

Speeds, Sizes, Times [optional]
Finetuning Time: Approximately 12 hours

Model Size: 15.5 GB (full model), ~120 MB (LoRA adapter)

Evaluation
Testing Data, Factors & Metrics
Testing Data: The model was tested on a separate, held-out validation set of code generation prompts.

Factors: Performance was evaluated on different programming languages (Python, C++, JS).

Metrics:

Pass@1: The percentage of prompts for which the model generated a correct and compilable solution on the first try.

Readability Score: An informal metric based on human evaluation of code style and clarity.

Results
Pass@1 (Overall): 45.2%

Pass@1 (Python): 55.1%

Readability: The generated code was generally readable and well-commented.

Summary
Model Examination
The model demonstrates strong performance in common code generation tasks, particularly for Python. It can produce functional and readable code snippets.

Environmental Impact
Hardware Type: 1 x NVIDIA A100 GPU

Hours used: 12 hours

Cloud Provider: Google Cloud

Compute Region: us-central1

Carbon Emitted: 1.05 kg CO2eq (estimated using the Machine Learning Impact calculator)

Technical Specifications [optional]
Model Architecture and Objective
The model is a decoder-only transformer architecture. Its objective is to predict the next token in a sequence, conditioned on the preceding tokens. The finetuning process adapted the base model to excel at generating code.

Citation [optional]
BibTeX
@misc{Arko007_my-awesome-code-assistant-v4,
  author = {Arko007},
  title = {my-awesome-code-assistant-v4},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/Arko007/my-awesome-code-assistant-v4}
}

APA
Arko007. (2024). my-awesome-code-assistant-v4. Hugging Face. Retrieved from https://huggingface.co/Arko007/my-awesome-code-assistant-v4

Model Card Authors [optional]
Arko007

Model Card Contact
[Email or other contact information]

Framework versions
PEFT 0.17.0