DevPearl-2x7B, an xtraordinary Mixture of Experts (MoE) for development
DevPearl-2x7B is a Mixture of Experts (MoE) made with the following models :
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
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
!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.
@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.