File size: 2,015 Bytes
e1c8d2e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
---
license: llama2
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- CodeMate
- Code
---

# **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).