README.md

---
license: cc-by-sa-4.0
tags:
- moe
- merge
- mergekit
- lazymergekit
- deepseek-ai/deepseek-coder-6.7b-instruct
- defog/sqlcoder-7b-2
- Python
- Javascript
- sql
base_model:
- deepseek-ai/deepseek-coder-6.7b-instruct
- defog/sqlcoder-7b-2
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
<center><img src='https://i.imgur.com/0xFTuAX.png' width='450px'></center>

# DevPearl-2x7B, an xtraordinary Mixture of Experts (MoE) for development

DevPearl-2x7B is a Mixture of Experts (MoE) made with the following models :
* [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct)
* [defog/sqlcoder-7b-2](https://huggingface.co/defog/sqlcoder-7b-2)

A Mixture of Experts (MoE) model represents a sophisticated architecture that amalgamates the capabilities of multiple specialized models to address a wide array of tasks within a unified framework. Within the realm of a MoE model tailored for a chat application, the integration of expertise spanning three distinct domains - chat, code, and mathematics - substantially enhances its capacity to furnish nuanced and precise responses to a diverse spectrum of user inquiries.

## Configuration

```yaml
base_model: codellama/CodeLlama-7b-Instruct-hf
experts:
  - source_model: deepseek-ai/deepseek-coder-6.7b-instruct
    positive_prompts:
      - "python"
      - "javascript"
      - "java"
  - source_model: defog/sqlcoder-7b-2
    positive_prompts:
      - "SQL"
```

## Usage

```python
!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "louisbrulenaudet/DevPearl-2x7B"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```

## Citing & Authors

If you use this code in your research, please use the following BibTeX entry.

```BibTeX
@misc{louisbrulenaudet2023,
  author =       {Louis Brulé Naudet},
  title =        {DevPearl-2x7B, an xtraordinary Mixture of Experts (MoE) for development},
  year =         {2024}
  howpublished = {\url{https://huggingface.co/louisbrulenaudet/DevPearl-2x7B}},
}
```

## Feedback

If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com).