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
1from transformers import AutoTokenizer
2import transformers
3import torch
4
5model = "medxiaorudan/CodeLlama_CPP_FineTuned"
6tokenizer = AutoTokenizer.from_pretrained(model)
7pipeline = transformers.pipeline(
8 "text-generation",
9 model=model,
10 torch_dtype=torch.float16,
11 device_map="auto",
12)
13
14prompt = """
15Use the Task below and write the Response, which is a programming code that can solve the Task.
16### Task:
17Generate 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.
18### Response:
19"""
20sequences_finetune = pipeline(
21 prompt,
22 do_sample=True,
23 top_k=10,
24 temperature=0.1,
25 top_p=0.95,
26 num_return_sequences=1,
27 eos_token_id=tokenizer.eos_token_id,
28 max_length=600,
29 add_special_tokens=False
30)
31for seq in sequences_finetune:
32 print(f"Result: {seq['generated_text']}")
Direct Use
[More Information Needed]
Downstream Use [optional]
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Out-of-Scope Use
[More Information Needed]
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
[More Information Needed]
Training Procedure
The detailed training report is
here.
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
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
[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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Model Card Authors [optional]
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Model Card Contact
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Framework versions
