Update README.md

README.md

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-license: mit
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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://github.com/deepseek-ai/DeepSeek-V2/blob/main/deepseek-v2-tech-report.pdf"><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. Model Downloads
+
+<div align="center">
+
+| **Model** | **Context Length** | **Download** |
+| :------------: | :------------: | :------------: |
+| DeepSeek-V2   | 128k   | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V2)   |
+| DeepSeek-V2-Chat (RL)   | 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.
+
+## 3. Evaluation Results
+### Base Model
+#### Standard Benchmark 
+
+<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>
+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 
+<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>
+
+#### 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 | 8.35 |
+| 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 |
+
+</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>
+
+## 4. 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>
+
+## 5. Chat Website
+You can chat with the DeepSeek-V2 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in)
+
+## 6. 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>
+
+
+## 7. 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)
+```
+
+## 8. 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.
+
+## 9. 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}
+}
+```
+
+## 10. Contact
+If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).