|
--- |
|
license: other |
|
license_name: deepseek-license |
|
license_link: LICENSE |
|
base_model: deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct |
|
pipeline_tag: text-generation |
|
--- |
|
<!-- markdownlint-disable first-line-h1 --> |
|
<!-- markdownlint-disable html --> |
|
<!-- markdownlint-disable no-duplicate-header --> |
|
|
|
# QuantFactory/DeepSeek-Coder-V2-Lite-Instruct-GGUF |
|
This is quantized version of [QuantFactory/DeepSeek-Coder-V2-Lite-Instruct-GGUF](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) created using llama.cpp |
|
|
|
# Model Description |
|
<div align="center"> |
|
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V2" /> |
|
</div> |
|
|
|
<p align="center"> |
|
<a href="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/paper.pdf"><b>Paper Link</b>👁️</a> |
|
</p> |
|
|
|
# DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence |
|
|
|
## 1. Introduction |
|
We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks. Compared to DeepSeek-Coder-33B, DeepSeek-Coder-V2 demonstrates significant advancements in various aspects of code-related tasks, as well as reasoning and general capabilities. Additionally, DeepSeek-Coder-V2 expands its support for programming languages from 86 to 338, while extending the context length from 16K to 128K. |
|
|
|
<p align="center"> |
|
<img width="100%" src="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/figures/performance.png?raw=true"> |
|
</p> |
|
|
|
|
|
In standard benchmark evaluations, DeepSeek-Coder-V2 achieves superior performance compared to closed-source models such as GPT4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro in coding and math benchmarks. The list of supported programming languages can be found [here](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/supported_langs.txt). |
|
|
|
## 2. Model Downloads |
|
|
|
We release the DeepSeek-Coder-V2 with 16B and 236B parameters based on the [DeepSeekMoE](https://arxiv.org/pdf/2401.06066) framework, which has actived parameters of only 2.4B and 21B , including base and instruct models, to the public. |
|
|
|
<div align="center"> |
|
|
|
| **Model** | **#Total Params** | **#Active Params** | **Context Length** | **Download** | |
|
| :-----------------------------: | :---------------: | :----------------: | :----------------: | :----------------------------------------------------------: | |
|
| DeepSeek-Coder-V2-Lite-Base | 16B | 2.4B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Base) | |
|
| DeepSeek-Coder-V2-Lite-Instruct | 16B | 2.4B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) | |
|
| DeepSeek-Coder-V2-Base | 236B | 21B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Base) | |
|
| DeepSeek-Coder-V2-Instruct | 236B | 21B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Instruct) | |
|
|
|
</div> |
|
|
|
|
|
## 3. Chat Website |
|
|
|
You can chat with the DeepSeek-Coder-V2 on DeepSeek's official website: [coder.deepseek.com](https://coder.deepseek.com/sign_in) |
|
|
|
## 4. API Platform |
|
We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/), and you can also pay-as-you-go at an unbeatable price. |
|
<p align="center"> |
|
<img width="40%" src="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/figures/model_price.jpg?raw=true"> |
|
</p> |
|
|
|
|
|
## 5. How to run locally |
|
**Here, we provide some examples of how to use DeepSeek-Coder-V2-Lite model. If you want to utilize DeepSeek-Coder-V2 in BF16 format for inference, 80GB*8 GPUs are required.** |
|
|
|
### Inference with Huggingface's Transformers |
|
You can directly employ [Huggingface's Transformers](https://github.com/huggingface/transformers) for model inference. |
|
|
|
#### Code Completion |
|
```python |
|
from transformers import AutoTokenizer, AutoModelForCausalLM |
|
import torch |
|
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True) |
|
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda() |
|
input_text = "#write a quick sort algorithm" |
|
inputs = tokenizer(input_text, return_tensors="pt").to(model.device) |
|
outputs = model.generate(**inputs, max_length=128) |
|
print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
|
``` |
|
|
|
#### Code Insertion |
|
```python |
|
from transformers import AutoTokenizer, AutoModelForCausalLM |
|
import torch |
|
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True) |
|
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda() |
|
input_text = """<｜fim▁begin｜>def quick_sort(arr): |
|
if len(arr) <= 1: |
|
return arr |
|
pivot = arr[0] |
|
left = [] |
|
right = [] |
|
<｜fim▁hole｜> |
|
if arr[i] < pivot: |
|
left.append(arr[i]) |
|
else: |
|
right.append(arr[i]) |
|
return quick_sort(left) + [pivot] + quick_sort(right)<｜fim▁end｜>""" |
|
inputs = tokenizer(input_text, return_tensors="pt").to(model.device) |
|
outputs = model.generate(**inputs, max_length=128) |
|
print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_text):]) |
|
``` |
|
|
|
#### Chat Completion |
|
|
|
```python |
|
from transformers import AutoTokenizer, AutoModelForCausalLM |
|
import torch |
|
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True) |
|
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda() |
|
messages=[ |
|
{ 'role': 'user', 'content': "write a quick sort algorithm in python."} |
|
] |
|
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) |
|
# tokenizer.eos_token_id is the id of <|EOT|> token |
|
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) |
|
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)) |
|
``` |
|
|
|
|
|
|
|
The complete chat template can be found within `tokenizer_config.json` located in the huggingface model repository. |
|
|
|
An example of chat template is as belows: |
|
|
|
```bash |
|
<｜begin▁of▁sentence｜>User: {user_message_1} |
|
|
|
Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2} |
|
|
|
Assistant: |
|
``` |
|
|
|
You can also add an optional system message: |
|
|
|
```bash |
|
<｜begin▁of▁sentence｜>{system_message} |
|
|
|
User: {user_message_1} |
|
|
|
Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2} |
|
|
|
Assistant: |
|
``` |
|
|
|
### Inference with vLLM (recommended) |
|
To utilize [vLLM](https://github.com/vllm-project/vllm) for model inference, please merge this Pull Request into your vLLM codebase: https://github.com/vllm-project/vllm/pull/4650. |
|
|
|
```python |
|
from transformers import AutoTokenizer |
|
from vllm import LLM, SamplingParams |
|
|
|
max_model_len, tp_size = 8192, 1 |
|
model_name = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct" |
|
tokenizer = AutoTokenizer.from_pretrained(model_name) |
|
llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True, enforce_eager=True) |
|
sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id]) |
|
|
|
messages_list = [ |
|
[{"role": "user", "content": "Who are you?"}], |
|
[{"role": "user", "content": "write a quick sort algorithm in python."}], |
|
[{"role": "user", "content": "Write a piece of quicksort code in C++."}], |
|
] |
|
|
|
prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list] |
|
|
|
outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params) |
|
|
|
generated_text = [output.outputs[0].text for output in outputs] |
|
print(generated_text) |
|
``` |
|
|
|
|
|
|
|
## 6. License |
|
|
|
This code repository is licensed under [the MIT License](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/LICENSE-CODE). The use of DeepSeek-Coder-V2 Base/Instruct models is subject to [the Model License](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/LICENSE-MODEL). DeepSeek-Coder-V2 series (including Base and Instruct) supports commercial use. |
|
|
|
|
|
## 7. Original Model Contact |
|
If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]). |
