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Let the Code Write Itself

DeepSeek Coder is a suite of code language models trained from scratch on 2T tokens consisting of 87% code and 13% natural language (English and Chinese). Models range in size from 1B to 33B parameters. It's pre-trained on a project-level code corpus using a 16K window size and a fill-in-the-blank task to support project-level code completion and infilling. The architecture is based on the transformer model. It focuses on superior code generation and understanding capabilities. It achieves state-of-the-art performance among open-source code models on benchmarks like HumanEval, MultiPL-E, MBPP, DS-1000, and APPS. It offers flexibility with different model sizes, allowing users to choose the best fit for their hardware and requirements. Advanced code completion supports project-level tasks.
DeepSeek Coder is a suite of code language models trained from scratch on 2T tokens consisting of 87% code and 13% natural language (English and Chinese).
Explore all tools that specialize in code completion. This domain focus ensures DeepSeek Coder delivers optimized results for this specific requirement.
Explore all tools that specialize in generate code. This domain focus ensures DeepSeek Coder delivers optimized results for this specific requirement.
Explore all tools that specialize in complete code. This domain focus ensures DeepSeek Coder delivers optimized results for this specific requirement.
Explore all tools that specialize in debug code. This domain focus ensures DeepSeek Coder delivers optimized results for this specific requirement.
Explore all tools that specialize in refactor code. This domain focus ensures DeepSeek Coder delivers optimized results for this specific requirement.
Explore all tools that specialize in review code. This domain focus ensures DeepSeek Coder delivers optimized results for this specific requirement.
Leverages a 16K window size and fill-in-the-blank training to understand and complete code across entire projects.
Trained on both English and Chinese natural language data, enabling code generation from prompts in either language.
The Instruct model is fine-tuned on 2B tokens of instruction data to improve code generation based on user instructions.
Offers models ranging from 1B to 33B parameters, allowing users to select a model that balances performance with resource constraints.
Pre-training task that enhances the model's ability to perform code infilling, which is useful for completing partially written code blocks.
Install the necessary dependencies: `pip install -r requirements.txt`
Import the required modules from transformers and torch.
Load the tokenizer and model using AutoTokenizer.from_pretrained and AutoModelForCausalLM.from_pretrained.
Specify the model name (e.g., "deepseek-ai/deepseek-coder-6.7b-base")
Move the model to the GPU using .cuda()
Prepare the input text using the tokenizer.
Generate the output using model.generate().
Decode the generated tokens to obtain the code.
All Set
Ready to go
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"DeepSeek Coder receives high praise for its code generation accuracy and performance."
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