diff --git a/README.md b/README.md index 15645de..cd082c7 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ Deepseek Coder comprises a series of code language models trained on both 87% code and 13% natural language in English and Chinese, with each model pre-trained on 2T tokens. We provide various sizes of the code model, ranging from 1B to 33B versions. Each model is pre-trained on project-level code corpus by employing a window size of 16K and a extra fill-in-the-blank task, to support project-level code completion and infilling. For coding capabilities, Deepseek Coder achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks.

-result +result

- **Massive Training Data**: Trained on 2T tokens, including 87% code and 13% linguistic data in both English and Chinese languages. @@ -207,10 +207,10 @@ print(tokenizer.decode(outputs[0])) ### 5. Evaluation Results We evaluate DeepSeek Coder on various coding-related benchmarks. -The `passk@1` results on HumanEval (Python and Multilingual), MBPP, DS-1000 are reported as follows: +Only `pass@1` results on HumanEval (Python and Multilingual), MBPP, DS-1000 are reported here:

-table +table

The result shows that DeepSeek-Coder-Base-33B significantly outperforms existing open-source code LLMs. Compared with CodeLLama34B, it leads by 7.9%, 9.3%, 10.8% and 5.9% respectively on HumanEval Python, HumanEval Multilingual, MBPP and DS-1000. diff --git a/pictures/.init b/pictures/.init deleted file mode 100644 index 8b13789..0000000 --- a/pictures/.init +++ /dev/null @@ -1 +0,0 @@ -