Instructions to use louisbrulenaudet/DevPearl-2x7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use louisbrulenaudet/DevPearl-2x7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="louisbrulenaudet/DevPearl-2x7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("louisbrulenaudet/DevPearl-2x7B")
model = AutoModelForCausalLM.from_pretrained("louisbrulenaudet/DevPearl-2x7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use louisbrulenaudet/DevPearl-2x7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "louisbrulenaudet/DevPearl-2x7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "louisbrulenaudet/DevPearl-2x7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/louisbrulenaudet/DevPearl-2x7B

SGLang

How to use louisbrulenaudet/DevPearl-2x7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "louisbrulenaudet/DevPearl-2x7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "louisbrulenaudet/DevPearl-2x7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "louisbrulenaudet/DevPearl-2x7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "louisbrulenaudet/DevPearl-2x7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use louisbrulenaudet/DevPearl-2x7B with Docker Model Runner:
```
docker model run hf.co/louisbrulenaudet/DevPearl-2x7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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}},
}