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from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
max_model_len, tp_size = 8192, 1
model_name_or_path = "deepseek-ai/DeepSeek-V2-Lite-Chat"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
llm = LLM(model=model_name_or_path, 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": "Translate the following content into Chinese directly: DeepSeek-V2 adopts innovative architectures to guarantee economical training and efficient inference."}],
[{"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)
LICENSE-CODE 0 → 100644
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MIT License
Copyright (c) 2023 DeepSeek
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
LICENSE-MODEL 0 → 100644
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DEEPSEEK LICENSE AGREEMENT
Version 1.0, 23 October 2023
Copyright (c) 2023 DeepSeek
Section I: PREAMBLE
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Attachment A
Use Restrictions
You agree not to use the Model or Derivatives of the Model:
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README.md 0 → 100644
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# llama3
## 论文
[deepseek-v2](https://arxiv.org/abs/2405.04434)
## 模型结构
<div align=center>
<img src="./doc/model.png"/>
</div>
## 环境配置
-v 路径、docker_name和imageID根据实际情况修改
### Docker（方法一）
```bash
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk24.04-py310
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=80G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
cd /your_code_path/deepseek-v2_pytorch
pip install -r requirements.txt
pip install -U huggingface_hub hf_transfer
export HF_ENDPOINT=https://hf-mirror.com
```
### Dockerfile（方法二）
```bash
cd docker
docker build --no-cache -t llama3:latest .
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=80G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
cd /your_code_path/deepseek-v2_pytorch
pip install -r requirements.txt
pip install -U huggingface_hub hf_transfer
export HF_ENDPOINT=https://hf-mirror.com
```
### Anaconda（方法三）
关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.hpccube.com/tool/)开发者社区下载安装。
```bash
DTK驱动: dtk24.04
python: python3.10
torch: 2.1.0
```
`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应`
其它非深度学习库安装方式如下：
```bash
pip install -r requirements.txt
pip install -U huggingface_hub hf_transfer
export HF_ENDPOINT=https://hf-mirror.com
```
## 数据集
暂无
## 训练
## 推理
### 精度
暂无
## 应用场景
### 算法类别
对话问答
### 热点应用行业
制造,广媒,家居,教育
## 预训练权重
模型目录结构如下：
```bash
├── model_save_path
│ ├── DeepSeek-V2
│ ├── LICENSE
│ ├── README.md
│ ├── config.json
│ ├── configuration_deepseek.py
│ ├── generation_config.json
│ ├── model-00001-of-000004.safetensors
│ ├── model-00002-of-000004.safetensors
│ ├── model-00003-of-000004.safetensors
│ ├── model-00004-of-000004.safetensors
│ ├── model.safetensors.index.json
│ ├── modeling_deepseek.py
│ ├── tokenization_deepseek_fast.py
│ ├── tokenizer.json
│ └── tokenizer_config.json
│ ├── DeepSeek-V2-Lite
│ ├── LICENSE
│ ├── README.md
│ ├── config.json
│ ├── configuration_deepseek.py
│ ├── generation_config.json
│ ├── model-00001-of-000055.safetensors
│ ├── model-00002-of-000055.safetensors
│ ├── model-00054-of-000055.safetensors
│ ...
│ ├── model-00055-of-000055.safetensors
│ ├── model.safetensors.index.json
│ ├── modeling_deepseek.py
│ ├── tokenization_deepseek_fast.py
│ ├── tokenizer.json
│ └── tokenizer_config.json
```
## 源码仓库及问题反馈
- https://developer.hpccube.com/codes/modelzoo/deepseek-v2_pytorch
## 参考资料
- https://github.com/deepseek-ai/DeepSeek-V2
- https://huggingface.co/deepseek-ai/DeepSeek-V2
- https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite
README_ori.md 0 → 100644
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<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->
<div align="center">
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek LLM" />
</div>
<hr>
<div align="center">
<a href="https://www.deepseek.com/" target="_blank">
<img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://chat.deepseek.com/" target="_blank">
<img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20LLM-536af5?color=536af5&logoColor=white?raw=true" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/deepseek-ai" target="_blank">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white?raw=true" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://discord.gg/Tc7c45Zzu5" target="_blank">
<img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da?raw=true" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg" target="_blank">
<img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white?raw=true"style="display: inline-block; vertical-align: middle;" />
</a>
<a href="https://twitter.com/deepseek_ai" target="_blank">
<img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white?raw=true" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="LICENSE-CODE">
<img alt="Code License" src="https://img.shields.io/badge/Code_License-MIT-f5de53?&color=f5de53?raw=true"style="display: inline-block; vertical-align: middle;">
</a>
<a href="LICENSE-MODEL">
<img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53?raw=true"style="display: inline-block; vertical-align: middle;">
</a>
</div>
<p align="center">
<a href="#2-model-downloads">Model Download</a> |
<a href="#3-evaluation-results">Evaluation Results</a> |
<a href="#4-model-architecture">Model Architecture</a> |
<a href="#6-api-platform">API Platform</a> |
<a href="#8-license">License</a> |
<a href="#9-citation">Citation</a>
</p>
<p align="center">
<a href="https://arxiv.org/abs/2405.04434"><b>Paper Link</b>👁️</a>
</p>
# DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
## 1. Introduction
Today, we’re introducing DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token. Compared with DeepSeek 67B, DeepSeek-V2 achieves stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times.
<p align="center">
<div style="display: flex; justify-content: center;">
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/activationparameters.png?raw=true" style="height:300px; width:auto; margin-right:10px">
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/trainingcost.png?raw=true" style="height:300px; width:auto; margin-left:10px">
</div>
</p>
We pretrained DeepSeek-V2 on a diverse and high-quality corpus comprising 8.1 trillion tokens. This comprehensive pretraining was followed by a process of Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to fully unleash the model's capabilities. The evaluation results validate the effectiveness of our approach as DeepSeek-V2 achieves remarkable performance on both standard benchmarks and open-ended generation evaluation.
## 2. News
- 2024.05.16: We released the DeepSeek-V2-Lite.
- 2024.05.06: We released the DeepSeek-V2.
## 3. Model Downloads
<div align="center">
| **Model** | **#Total Params** | **#Activated Params** | **Context Length** | **Download** |
| :------------: | :------------: | :------------: | :------------: | :------------: |
| DeepSeek-V2-Lite | 16B | 2.4B | 32k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite) |
| DeepSeek-V2-Lite-Chat (SFT) | 16B | 2.4B | 32k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat) |
| DeepSeek-V2 | 236B | 21B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V2) |
| DeepSeek-V2-Chat (RL) | 236B | 21B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat) |
</div>
Due to the constraints of HuggingFace, the open-source code currently experiences slower performance than our internal codebase when running on GPUs with Huggingface. To facilitate the efficient execution of our model, we offer a dedicated vllm solution that optimizes performance for running our model effectively.
## 4. Evaluation Results
### Base Model
#### Standard Benchmark (Models larger than 67B)
<div align="center">
| **Benchmark** | **Domain** | **LLaMA3 70B** | **Mixtral 8x22B** | **DeepSeek-V1 (Dense-67B)** | **DeepSeek-V2 (MoE-236B)** |
|:-----------:|:--------:|:------------:|:---------------:|:-------------------------:|:------------------------:|
| **MMLU** | English | 78.9 | 77.6 | 71.3 | 78.5 |
| **BBH** | English | 81.0 | 78.9 | 68.7 | 78.9 |
| **C-Eval** | Chinese | 67.5 | 58.6 | 66.1 | 81.7 |
| **CMMLU** | Chinese | 69.3 | 60.0 | 70.8 | 84.0 |
| **HumanEval** | Code | 48.2 | 53.1 | 45.1 | 48.8 |
| **MBPP** | Code | 68.6 | 64.2 | 57.4 | 66.6 |
| **GSM8K** | Math | 83.0 | 80.3 | 63.4 | 79.2 |
| **Math** | Math | 42.2 | 42.5 | 18.7 | 43.6 |
</div>
#### Standard Benchmark (Models smaller than 16B)
<div align="center">
| **Benchmark** | **Domain** | **DeepSeek 7B (Dense)** | **DeepSeekMoE 16B** | **DeepSeek-V2-Lite (MoE-16B)** |
|:-------------:|:----------:|:--------------:|:-----------------:|:--------------------------:|
| **Architecture** | - | MHA+Dense | MHA+MoE | MLA+MoE |
| **MMLU** | English | 48.2 | 45.0 | 58.3 |
| **BBH** | English | 39.5 | 38.9 | 44.1 |
| **C-Eval** | Chinese | 45.0 | 40.6 | 60.3 |
| **CMMLU** | Chinese | 47.2 | 42.5 | 64.3 |
| **HumanEval** | Code | 26.2 | 26.8 | 29.9 |
| **MBPP** | Code | 39.0 | 39.2 | 43.2 |
| **GSM8K** | Math | 17.4 | 18.8 | 41.1 |
| **Math** | Math | 3.3 | 4.3 | 17.1 |
</div>
For more evaluation details, such as few-shot settings and prompts, please check our paper.
#### Context Window
<p align="center">
<img width="80%" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/niah.png?raw=true">
</p>
Evaluation results on the ``Needle In A Haystack`` (NIAH) tests. DeepSeek-V2 performs well across all context window lengths up to **128K**.
### Chat Model
#### Standard Benchmark (Models larger than 67B)
<div align="center">
| Benchmark | Domain | QWen1.5 72B Chat | Mixtral 8x22B | LLaMA3 70B Instruct | DeepSeek-V1 Chat (SFT) | DeepSeek-V2 Chat (SFT) | DeepSeek-V2 Chat (RL) |
|:-----------:|:----------------:|:------------------:|:---------------:|:---------------------:|:-------------:|:-----------------------:|:----------------------:|
| **MMLU** | English | 76.2 | 77.8 | 80.3 | 71.1 | 78.4 | 77.8 |
| **BBH** | English | 65.9 | 78.4 | 80.1 | 71.7 | 81.3 | 79.7 |
| **C-Eval** | Chinese | 82.2 | 60.0 | 67.9 | 65.2 | 80.9 | 78.0 |
| **CMMLU** | Chinese | 82.9 | 61.0 | 70.7 | 67.8 | 82.4 | 81.6 |
| **HumanEval** | Code | 68.9 | 75.0 | 76.2 | 73.8 | 76.8 | 81.1 |
| **MBPP** | Code | 52.2 | 64.4 | 69.8 | 61.4 | 70.4 | 72.0 |
| **LiveCodeBench (0901-0401)** | Code | 18.8 | 25.0 | 30.5 | 18.3 | 28.7 | 32.5 |
| **GSM8K** | Math | 81.9 | 87.9 | 93.2 | 84.1 | 90.8 | 92.2 |
| **Math** | Math | 40.6 | 49.8 | 48.5 | 32.6 | 52.7 | 53.9 |
</div>
#### Standard Benchmark (Models smaller than 16B)
<div align="center">
| Benchmark | Domain | DeepSeek 7B Chat (SFT) | DeepSeekMoE 16B Chat (SFT) | DeepSeek-V2-Lite 16B Chat (SFT) |
|:-----------:|:----------------:|:------------------:|:---------------:|:---------------------:|
| **MMLU** | English | 49.7 | 47.2 | 55.7 |
| **BBH** | English | 43.1 | 42.2 | 48.1 |
| **C-Eval** | Chinese | 44.7 | 40.0 | 60.1 |
| **CMMLU** | Chinese | 51.2 | 49.3 | 62.5 |
| **HumanEval** | Code | 45.1 | 45.7 | 57.3 |
| **MBPP** | Code | 39.0 | 46.2 | 45.8 |
| **GSM8K** | Math | 62.6 | 62.2 | 72.0 |
| **Math** | Math | 14.7 | 15.2 | 27.9 |
</div>
#### English Open Ended Generation Evaluation
We evaluate our model on AlpacaEval 2.0 and MTBench, showing the competitive performance of DeepSeek-V2-Chat-RL on English conversation generation.
<p align="center">
<img width="50%" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/mtbench.png?raw=true" />
</p>
#### Chinese Open Ended Generation Evaluation
**Alignbench** (https://arxiv.org/abs/2311.18743)
<div align="center">
| **模型** | **开源/闭源** | **总分** | **中文推理** | **中文语言** |
| :---: | :---: | :---: | :---: | :---: |
| gpt-4-1106-preview | 闭源 | 8.01 | 7.73 | 8.29 |
| DeepSeek-V2 Chat (RL) | 开源 | 7.91 | 7.45 | 8.36 |
| erniebot-4.0-202404 (文心一言) | 闭源 | 7.89 | 7.61 | 8.17 |
| DeepSeek-V2 Chat (SFT) | 开源 | 7.74 | 7.30 | 8.17 |
| gpt-4-0613 | 闭源 | 7.53 | 7.47 | 7.59 |
| erniebot-4.0-202312 (文心一言) | 闭源 | 7.36 | 6.84 | 7.88 |
| moonshot-v1-32k-202404 (月之暗面) | 闭源 | 7.22 | 6.42 | 8.02 |
| Qwen1.5-72B-Chat (通义千问) | 开源 | 7.19 | 6.45 | 7.93 |
| DeepSeek-67B-Chat | 开源 | 6.43 | 5.75 | 7.11 |
| Yi-34B-Chat (零一万物) | 开源 | 6.12 | 4.86 | 7.38 |
| gpt-3.5-turbo-0613 | 闭源 | 6.08 | 5.35 | 6.71 |
| DeepSeek-V2-Lite 16B Chat | 开源 | 6.01 | 4.71 | 7.32 |
</div>
#### Coding Benchmarks
We evaluate our model on LiveCodeBench (0901-0401), a benchmark designed for live coding challenges. As illustrated, DeepSeek-V2 demonstrates considerable proficiency in LiveCodeBench, achieving a Pass@1 score that surpasses several other sophisticated models. This performance highlights the model's effectiveness in tackling live coding tasks.
<p align="center">
<img width="50%" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/code_benchmarks.png?raw=true">
</p>
## 5. Model Architecture
DeepSeek-V2 adopts innovative architectures to guarantee economical training and efficient inference：
- For attention, we design MLA (Multi-head Latent Attention), which utilizes low-rank key-value union compression to eliminate the bottleneck of inference-time key-value cache, thus supporting efficient inference.
- For Feed-Forward Networks (FFNs), we adopt DeepSeekMoE architecture, a high-performance MoE architecture that enables training stronger models at lower costs.
<p align="center">
<img width="90%" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/architecture.png?raw=true" />
</p>
## 6. Chat Website
You can chat with the DeepSeek-V2 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in)
## 7. API Platform
We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/). Sign up for over millions of free tokens. 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-V2/blob/main/figures/model_price.png?raw=true">
</p>
## 8. How to run locally
**To utilize DeepSeek-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.
#### Text Completion
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/DeepSeek-V2"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
# `max_memory` should be set based on your devices
max_memory = {i: "75GB" for i in range(8)}
# `device_map` cannot be set to `auto`
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map="sequential", torch_dtype=torch.bfloat16, max_memory=max_memory, attn_implementation="eager")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
text = "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
#### Chat Completion
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/DeepSeek-V2-Chat"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
# `max_memory` should be set based on your devices
max_memory = {i: "75GB" for i in range(8)}
# `device_map` cannot be set to `auto`
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map="sequential", torch_dtype=torch.bfloat16, max_memory=max_memory, attn_implementation="eager")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
messages = [
{"role": "user", "content": "Write a piece of quicksort code in C++"}
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
print(result)
```
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, 8
model_name = "deepseek-ai/DeepSeek-V2-Chat"
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": "Translate the following content into Chinese directly: DeepSeek-V2 adopts innovative architectures to guarantee economical training and efficient inference."}],
[{"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)
```
### LangChain Support
Since our API is compatible with OpenAI, you can easily use it in [langchain](https://www.langchain.com/).
Here is an example:
```
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
model='deepseek-chat',
openai_api_key=<your-deepseek-api-key>,
openai_api_base='https://api.deepseek.com/v1',
temperature=0.85,
max_tokens=8000)
```
## 9. License
This code repository is licensed under [the MIT License](LICENSE-CODE). The use of DeepSeek-V2 Base/Chat models is subject to [the Model License](LICENSE-MODEL). DeepSeek-V2 series (including Base and Chat) supports commercial use.
## 10. Citation
```
@misc{deepseekv2,
title={DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model},
author={DeepSeek-AI},
year={2024},
eprint={2405.04434},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## 11. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).
chat_completion.py 0 → 100644
View file @ d3471cd8
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name_or_path = "/home/DeepSeek-V2/DeepSeek-V2-Lite-Chat"
# model_name_or_path = "deepseek-ai/DeepSeek-V2-Lite-Chat"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
model.generation_config = GenerationConfig.from_pretrained(model_name_or_path)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
messages = [
{"role": "user", "content": "Write a piece of quicksort code in C++"}
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
print("result", result)
\ No newline at end of file
figures/badge.svg 0 → 100644
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