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Deepseek Coder V2 Lite Instruct
Overview :
DeepSeek-Coder-V2 is an open-source Mixture-of-Experts code language model, whose performance rivals that of GPT4-Turbo. It excels in code-specific tasks. Further pre-trained with an additional 60 billion tokens, it enhances coding and mathematical reasoning abilities while maintaining comparable performance on general language tasks. Compared to DeepSeek-Coder-33B, it shows significant improvements across code-related tasks, reasoning, and general capabilities. Moreover, it supports 338 programming languages (an expansion from 86) and boasts a context length extended to 128K from 16K.
Target Users :
DeepSeek-Coder-V2 is suitable for developers and data scientists who need to perform code generation, code reasoning, and mathematical problem solving. It empowers users to boost their efficiency in programming and algorithm development by leveraging its powerful language understanding and generation capabilities.
Use Cases
Developers use DeepSeek-Coder-V2 to generate code for a Quick Sort algorithm.
Data scientists leverage the model for complex mathematical formula reasoning.
Educators utilize the model to assist in programming education, enhancing student learning efficiency.
Features
Supports code generation in 338 programming languages.
Context length supports up to 128K.
Enables local inference through Huggingface's Transformers.
Offers recommender inference through vLLM, requiring the merging of specific Pull Requests.
Provides an interactive DeepSeek official chat website.
Offers an OpenAI-compatible API platform with free tokens and on-demand paid options.
How to Use
1. Visit the DeepSeek-Coder-V2 Huggingface page.
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2. Download the desired DeepSeek-Coder-V2 version according to your needs.
3. Conduct local model inference using the Huggingface Transformers library.
4. Interact with the model through the DeepSeek official chat website.
5. Register and utilize the DeepSeek platform's OpenAI-compatible API.
6. Compose input text based on your specific task and leverage the model for code generation or reasoning completion.
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