CodeMate-v0.1
CodeMate-v0.1 is an intelligent programming assistant developed by CodeMate. This model aims to assist users in generating high-quality code solutions for programming problems. Please note that this model is currently in version 0.1.
Model Details
Training Data: Exclusively fine-tuned on a proprietary dataset of 1.8 billion tokens of high-quality programming problems and solutions.
The dataset was generated manually and is internal to CodeMate.
Training Techniques: The model was fine-tuned using Flash Attention 2, trained over 15 hours on 40 A100-80GB GPUs.
A sequence length of 8096 tokens was used during training.
Multilingual Support: CodeMate-v0.1 is proficient in multiple programming languages, including Python, C/C++, TypeScript, Java, and more.
How to Get Started with the Model
Make sure to install Transformers from the main git branch:
pip install git+https://github.com/huggingface/transformers.git
How to Prompt the Model
This model accepts prompts in the Alpaca/Vicuna instruction format. For example:
### System Prompt
You are an intelligent programming assistant.
### User Message
Implement a linked list in C++
### Assistant
...
Load the Model:
To load the model, utilize the following Python script:
from transformers import AutoTokenizer, AutoModelForCausalLM
# Initialize the model
model_path = "codemateai/CodeMate-v0.1"
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_path)
# ... generate response ...
Bias, Risks, and Limitations
This model has undergone very limited testing. CodeMate recommends additional safety testing before any real-world deployments.
For more information and updates, visit the CodeMate website.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 58.39 |
AI2 Reasoning Challenge (25-Shot) | 55.55 |
HellaSwag (10-Shot) | 78.03 |
MMLU (5-Shot) | 55.31 |
TruthfulQA (0-shot) | 48.64 |
Winogrande (5-shot) | 72.61 |
GSM8k (5-shot) | 40.18 |
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Evaluation results
- pass@1 on HumanEvalself-reported74.9%
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard55.550
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard78.030
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard55.310
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard48.640
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard72.610
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard40.180