deepseek moe 16b base

Introduction

DeepSeekMoE is a model designed for text generation, leveraging the capabilities of transformers. The model aims to provide efficient and accurate text completion and generation tasks.

Architecture

The DeepSeekMoE model is based on a mixture of experts architecture, specifically tailored for handling complex text generation tasks. It uses a transformer model as its core framework, allowing it to process and generate text with a high degree of accuracy.

Training

The model has been pre-trained on a vast corpus of text data, ensuring it can handle various text generation tasks. Training details, including datasets and specific methodologies, are not explicitly mentioned but are aligned with standard practices in the transformer-based model training domain.

Guide: Running Locally

1. Environment Setup: Ensure you have Python and PyTorch installed. Use a virtual environment to manage dependencies.

2. Install Transformers:

   pip install transformers
   

3. Load the Model: Use the following Python script to load and run the model for text completion tasks:

   import torch
   from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
   
   model_name = "deepseek-ai/deepseek-moe-16b-base"
   tokenizer = AutoTokenizer.from_pretrained(model_name)
   model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
   model.generation_config = GenerationConfig.from_pretrained(model_name)
   model.generation_config.pad_token_id = model.generation_config.eos_token_id
   
   text = "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is"
   inputs = tokenizer(text, return_tensors="pt")
   outputs = model.generate(**inputs.to(model.device), max_new_tokens=100)
   
   result = tokenizer.decode(outputs[0], skip_special_tokens=True)
   print(result)
   

4. Cloud GPUs: For optimal performance, consider using cloud GPU services such as AWS, Google Cloud, or Azure to handle the model's computational requirements.

License

The code repository is licensed under the MIT License, allowing for flexible use and modification. However, the use of the DeepSeekMoE model itself is governed by a specific model license, which supports commercial use. For more information, refer to the LICENSE-MODEL.