safetensors llama 8-bit bitsandbytes region:us
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Quantization made by Richard Erkhov.

[Github](https://github.com/RichardErkhov)

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cac-v0.1 - bnb 8bits
- Model creator: https://huggingface.co/codemateai/
- Original model: https://huggingface.co/codemateai/cac-v0.1/




Original model description:
---
base_model:
- deepseek-ai/deepseek-coder-6.7b-base
library_name: transformers

---
# CAC-v0.1
CAC is a large language model with 6.7B parameters specifically finetuned for code completions.
CAC, although trained for code autocompletion, can also be used for other code related tasks such as:
- Generation
- Summarization
- Translation
- Question Answering
- Optimization
- Debugging
- Code Review
and more.

---

This is the very first version of CAC (0.1) and is still under development. For this version, we chose to go ahead with DeepSeek-Coder-6.7B as the base model.

---

## 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.

- A sequence length of 8096 tokens was used during training.

- Multilingual Support: CAC-v0.1 is proficient in multiple programming languages, including Python, C/C++, TypeScript, Java, and more.

---

## Load the model with Transformers:

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
...
```

You can also use the Mistral chat template for conversations:

```
<s>[INST] .... [/INST] ... </s>
```

#### Load the model:
```
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 ...
```
---

## 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).

