---
language:
- en
license: llama2
library_name: transformers
tags:
- CodeMate
- Code
- CodeLLaMa
pipeline_tag: text-generation
model-index:
- name: CodeMate-v0.1
  results:
  - task:
      type: text-generation
    dataset:
      name: HumanEval
      type: openai_humaneval
    metrics:
    - type: pass@1
      value: 74.9%
      name: pass@1
      verified: false
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 55.55
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=codemateai/CodeMate-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 78.03
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=codemateai/CodeMate-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 55.31
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=codemateai/CodeMate-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 48.64
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=codemateai/CodeMate-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 72.61
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=codemateai/CodeMate-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 40.18
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=codemateai/CodeMate-v0.1
      name: Open LLM Leaderboard
---


# **CodeMate-v0.1**

CodeMate-v0.1 is an intelligent programming assistant developed by [CodeMate](https://codemate.ai).
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:

```bash
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:

```markdown
### 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:

```python
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](https://codemate.ai).
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_codemateai__CodeMate-v0.1)

|             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|