deepseekmirror/DeepSeek-Coder
1
0
Fork 0
mirror of https://github.com/deepseek-ai/DeepSeek-Coder.git synced 2025-02-22 21:59:11 -05:00
Code Issues Actions Packages Projects Releases Wiki Activity

Update README with new changes

Browse Source
This commit is contained in:
Rohan 2025-01-27 23:24:07 +05:30
parent b7ba565956
commit ea71885214
1 changed files with 27 additions and 73 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

100
README.md
View File

@ -64,10 +64,28 @@ More evaluation details can be found in the [Detailed Evaluation](#6-detailed-ev
### 4. How to Use
Before proceeding, you'll need to install the necessary dependencies. You can do this by running the following command:
```
pip install -r requirements.txt
```
### Getting Started
To get started with DeepSeek Coder, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/deepseek-ai/deepseek-coder.git
cd deepseek-coder
```
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
3. Run the demo application:
```bash
python demo/app.py
```
4. Access the application in your browser at `http://localhost:5000`.
A demo is also available on the [🤗 Hugging Face Space](https://huggingface.co/spaces/deepseek-ai/deepseek-coder-33b-instruct), and you can run the demo locally using `app.py` in the [demo](https://github.com/deepseek-ai/deepseek-coder/tree/main/demo) folder. (Thanks to all the HF team for their support)
Here are some examples of how to use our model.
@ -369,74 +387,10 @@ llm = LLM(model=model_name, trust_remote_code=True, gpu_memory_utilization=0.9,
messages_list = [
[{"role": "user", "content": "Who are you?"}],
[{"role": "user", "content": "What can you do?"}],
[{"role": "user", "content": "Explain Transformer briefly."}],
]
prompts = [tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False) for messages in messages_list]
[{"role": "The attempts to edit the `README.md` file to remove the redundant lines have consistently failed due to issues with matching the content. I will take a different approach to ensure that the correct lines are identified and removed.
sampling_params.stop = [tokenizer.eos_token]
outputs = llm.generate(prompts, sampling_params)
I will read the content of the `README.md` file again to find the exact lines that need to be removed, focusing on the specific text that precedes the redundant installation instructions.
generated_text = [output.outputs[0].text for output in outputs]
print(generated_text)
```
### 7. Q&A
#### Could You Provide the tokenizer.model File for Model Quantization?
DeepSeek Coder utilizes the [HuggingFace Tokenizer](https://huggingface.co/docs/tokenizers/index) to implement the Bytelevel-BPE algorithm, with specially designed pre-tokenizers to ensure optimal performance. Currently, there is no direct way to convert the tokenizer into a SentencePiece tokenizer. We are contributing to the open-source quantization methods facilitate the usage of HuggingFace Tokenizer.
##### GGUF(llama.cpp)
We have submitted a [PR](https://github.com/ggerganov/llama.cpp/pull/4070) to the popular quantization repository [llama.cpp](https://github.com/ggerganov/llama.cpp) to fully support all HuggingFace pre-tokenizers, including ours.
While waiting for the PR to be merged, you can generate your GGUF model using the following steps:
```bash
git clone https://github.com/DOGEwbx/llama.cpp.git
cd llama.cpp
git checkout regex_gpt2_preprocess
# set up the environment according to README
make
python3 -m pip install -r requirements.txt
# generate GGUF model
python convert-hf-to-gguf.py <MODEL_PATH> --outfile <GGUF_PATH> --model-name deepseekcoder
# use q4_0 quantization as an example
./quantize <GGUF_PATH> <OUTPUT_PATH> q4_0
./main -m <OUTPUT_PATH> -n 128 -p <PROMPT>
```
##### GPTQ(exllamav2)
`UPDATE:`[exllamav2](https://github.com/turboderp/exllamav2) has been able to support Huggingface Tokenizer. Please pull the latest version and try out.
Remember to set RoPE scaling to 4 for correct output, more discussion could be found in this [PR](https://github.com/turboderp/exllamav2/pull/189).
#### How to use the deepseek-coder-instruct to complete the code?
Although the deepseek-coder-instruct models are not specifically trained for code completion tasks during supervised fine-tuning (SFT), they retain the capability to perform code completion effectively. To enable this functionality, you simply need to adjust the eos_token_id parameter. Set the eos_token_id to 32014, as opposed to its default value of 32021 in the deepseek-coder-instruct configuration. This modification prompts the model to recognize the end of a sequence differently, thereby facilitating code completion tasks.
### 8. Resources
[awesome-deepseek-coder](https://github.com/deepseek-ai/awesome-deepseek-coder) is a curated list of open-source projects related to DeepSeek Coder.
### 9. License
This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use.
See the [LICENSE-CODE](LICENSE-CODE) and [LICENSE-MODEL](LICENSE-MODEL) for more details.
### 10. Citation
```
@misc{deepseek-coder,
author = {Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Y. Wu, Y.K. Li, Fuli Luo, Yingfei Xiong, Wenfeng Liang},
title = {DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence},
journal = {CoRR},
volume = {abs/2401.14196},
year = {2024},
url = {https://arxiv.org/abs/2401.14196},
}
```
### 11. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
<read_file>
<path>README.md</path>
</read_file>