85 lines
2.7 KiB
Markdown
85 lines
2.7 KiB
Markdown
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---
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license: cc-by-sa-4.0
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tags:
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- moe
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- merge
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- mergekit
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- lazymergekit
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- deepseek-ai/deepseek-coder-6.7b-instruct
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- defog/sqlcoder-7b-2
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- Python
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- Javascript
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- sql
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base_model:
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- deepseek-ai/deepseek-coder-6.7b-instruct
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- defog/sqlcoder-7b-2
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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---
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<center><img src='https://i.imgur.com/0xFTuAX.png' width='450px'></center>
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# DevPearl-2x7B, an xtraordinary Mixture of Experts (MoE) for development
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DevPearl-2x7B is a Mixture of Experts (MoE) made with the following models :
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* [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct)
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* [defog/sqlcoder-7b-2](https://huggingface.co/defog/sqlcoder-7b-2)
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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.
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## Configuration
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```yaml
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base_model: codellama/CodeLlama-7b-Instruct-hf
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experts:
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- source_model: deepseek-ai/deepseek-coder-6.7b-instruct
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positive_prompts:
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- "python"
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- "javascript"
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- "java"
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- source_model: defog/sqlcoder-7b-2
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positive_prompts:
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- "SQL"
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```
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## Usage
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```python
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!pip install -qU transformers bitsandbytes accelerate
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from transformers import AutoTokenizer
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import transformers
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import torch
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model = "louisbrulenaudet/DevPearl-2x7B"
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tokenizer = AutoTokenizer.from_pretrained(model)
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
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)
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messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
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prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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```
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## Citing & Authors
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If you use this code in your research, please use the following BibTeX entry.
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```BibTeX
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@misc{louisbrulenaudet2023,
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author = {Louis Brulé Naudet},
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title = {DevPearl-2x7B, an xtraordinary Mixture of Experts (MoE) for development},
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year = {2024}
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howpublished = {\url{https://huggingface.co/louisbrulenaudet/DevPearl-2x7B}},
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}
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```
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## Feedback
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If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com).
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