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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 [[email protected]](mailto:[email protected]). |
