deepseek moe 16b chat

Introduction

DeepSeekMoE is a model developed by DeepSeek-AI for conversational tasks. It leverages a large-scale architecture to provide text generation capabilities, making it suitable for chat applications.

Architecture

The model, referred to as deepseek-moe-16b-chat, is built using the Transformers library and is available on Hugging Face. It utilizes a mixture of experts (MoE) approach, enhancing its ability to handle complex conversational inputs and generate coherent responses.

Training

Details about the training process are not explicitly provided in the documentation. However, the model's architecture suggests it has been trained on a diverse dataset to optimize its conversational capabilities and text generation performance.

Guide: Running Locally

1. Environment Setup:

   • Install PyTorch and Transformers libraries.
   • Use AutoTokenizer and AutoModelForCausalLM from Transformers to load the model.

2. Model Usage:

   • Load the model using the identifier deepseek-ai/deepseek-moe-16b-chat.
   • Configure the generation settings and prepare input messages.
   • Use the model's generate method to obtain responses.

3. Example Code:

   import torch
   from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
   
   model_name = "deepseek-ai/deepseek-moe-16b-chat"
   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
   
   messages = [{"role": "user", "content": "Who are you?"}]
   input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
   outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
   
   result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
   print(result)
   

4. Cloud GPUs:

   • Consider using cloud services like AWS, GCP, or Azure for GPU resources to handle the model's computational requirements efficiently.

License

The DeepSeekMoE model is governed by its own Model License, which permits commercial use. The code repository is available under the MIT License. For detailed licensing information, refer to the LICENSE-MODEL.