Deep Seek V2.5 1210

Introduction

DeepSeek-V2.5-1210 is an enhanced iteration of the DeepSeek-V2.5 model, showcasing notable improvements in mathematical performance, coding accuracy, and writing and reasoning capabilities. Notable benchmarks include advancements in MATH-500, LiveCodebench, and internal datasets. The model also optimizes user experience for file upload and webpage summarization.

Architecture

DeepSeek-V2.5-1210 employs innovative architectures specifically designed to enhance performance in language model applications. The model is part of the DeepSeek series known for its efficiency and strength in various tasks.

Training

The model's training included performance enhancements across benchmarks in mathematics, coding, and reasoning tasks. Although detailed training processes are not specified, improvements in these areas indicate a rigorous refinement of model parameters and capabilities.

Guide: Running Locally

To run DeepSeek-V2.5-1210 locally, follow these steps:

1. Requirements: Use 80GB*8 GPUs for inference.
2. Environment Setup: Install Hugging Face Transformers and other dependencies.
3. Model Loading:

   import torch
   from transformers import AutoTokenizer, AutoModelForCausalLM
   
   model_name = "deepseek-ai/DeepSeek-V2.5-1210"
   tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
   max_memory = {i: "75GB" for i in range(8)}
   model = AutoModelForCausalLM.from_pretrained(
       model_name, trust_remote_code=True, device_map="sequential",
       torch_dtype=torch.bfloat16, max_memory=max_memory)
   

4. Running Inference: Use predefined prompts and generation configurations to interact with the model.
5. Recommended Cloud GPUs: Consider using cloud GPU services like AWS, Google Cloud, or Azure for resource-intensive tasks.

License

The code repository is released under the MIT License. Usage of DeepSeek-V2 Base/Chat models is governed by a specific Model License, allowing for commercial use. For more details, refer to the model's license here.