medxiaorudan/CodeLlama_CPP_FineTuned

Model Card for Model ID

Model Details

Model Description

This model has been fine-tuned using the CodeLlama base, incorporating C++ code sourced from the 'codeparrot/xlcost-text-to-code' dataset. It possesses the capability to generate C++ code based on provided 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]

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

Uses

from transformers import AutoTokenizer
import transformers
import torch

model = "medxiaorudan/CodeLlama_CPP_FineTuned" 
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

prompt = """
Use the Task below and write the Response, which is a programming code that can solve the Task.
### Task:
Generate a C++ program that accepts numeric input from the user and maintains a record of previous user inputs with timestamps. Ensure the program sorts the user inputs in ascending order based on the provided numeric input. Enhance the program to display timestamps along with the sorted user inputs.
### Response:
"""
sequences_finetune = pipeline(
    prompt,
    do_sample=True,
    top_k=10,
    temperature=0.1,
    top_p=0.95,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
    max_length=600,
    add_special_tokens=False
)
for seq in sequences_finetune:
    print(f"Result: {seq['generated_text']}")

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

[More Information Needed]

Recommendations

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.

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

• Training regime: [bf16]

Speeds, Sizes, Times [optional]

[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

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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 presented in Lacoste et al. (2019).

• 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

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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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Framework versions

• PEFT 0.7.1