README.md

---
library_name: peft
base_model: codellama/CodeLlama-7b-hf
license: llama2
dataset:
  type: codeparrot/xlcost-text-to-code
  name: xlcost
tags:
- code
---

# Model Card for Model ID

## Model Details

### Model Description

This model is fine-tuned base CodeLlama with C++ code from the 'codeparrot/xlcost-text-to-code' dataset. It can generate C++ code with specific task descriptions.
If you get the error "ValueError: Tokenizer class CodeLlamaTokenizer does not exist or is not currently imported." make sure your Transformer version is 4.33.0 and accelerate>=0.20.3. 


- **Developed by:** [Rudan XIAO]
- **Model type:** [code generation]
- **License:** [llama2]
- **Finetuned from model [optional]:** [codellama/CodeLlama-7b-hf]

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [https://github.com/medxiaorudan/CodeGeneration]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[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

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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

https://huggingface.co/datasets/codeparrot/xlcost-text-to-code

[More Information Needed]

### Training Procedure 

The detailed training report is [here](https://wandb.ai/medxiaorudan/CodeLlama_finetune_CPP?workspace=user-medxiaorudan).

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

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

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

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

I have use the Catch2 unit test framework for generated C++ code snippets correctness verification.
Todo: Use the pass@k metric with the HumanEval-X dataset to verify the performance of the model.

### Testing Data, Factors & Metrics

#### Testing Data

https://huggingface.co/datasets/THUDM/humaneval-x

[More Information Needed]

#### Factors

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

[More Information Needed]

#### Metrics

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

[More Information Needed]

### Results

[More Information Needed]

#### 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 -->
I used 4 NVIDIA A40-48Q GPU server configured with Python 3.10 and Cuda 12.2 to run the code in this article. It ran for about eight hours. 

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:** [NVIDIA A40-48Q GPU]
- **Hours used:** [8]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]


### Framework versions

- PEFT 0.7.1