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README.md

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+---
+license: other
+license_name: deepseek
+license_link: https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-MODEL
+---
+
+<!-- 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-V2" />
+</div>
+<hr>
+<div align="center" style="line-height: 1;">
+  <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
+    <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" style="margin: 2px;">
+    <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V2-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;">
+    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+<div align="center" style="line-height: 1;">
+  <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;">
+    <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;">
+    <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;">
+    <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+<div align="center" style="line-height: 1;">
+  <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-CODE" style="margin: 2px;">
+    <img alt="Code License" src="https://img.shields.io/badge/Code_License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-MODEL" style="margin: 2px;">
+    <img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+<p align="center">
+  <a href="https://arxiv.org/abs/2405.04434"><b>Paper Link</b>👁️</a>
+</p>
+
+# DeepSeek-V2.5
+
+## 1. Introduction
+
+DeepSeek-V2.5 is an upgraded version that combines DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct. The new model integrates the general and coding abilities of the two previous versions.
+For model details, please visit [DeepSeek-V2 page](https://github.com/deepseek-ai/DeepSeek-V2) for more information.
+
+DeepSeek-V2.5 better aligns with human preferences and has been optimized in various aspects, including writing and instruction following:
+
+- ArenaHard winrate increased from 68.3% to 76.3%
+- AlpacaEval 2.0 LC winrate increased from 46.61% to 50.52%
+- MT-Bench score increased from 8.84 to 9.02
+- AlignBench score increased from 7.88 to 8.04
+
+DeepSeek-V2.5 further enhances code generation capabilities, optimizing for common programming application scenarios, and achieving the following results on benchmarks:
+
+- HumanEval: 89%
+- LiveCodeBench (January - September): 41%
+
+## 2. How to run locally
+
+**To utilize DeepSeek-V2.5 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.
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
+
+model_name = "deepseek-ai/DeepSeek-V2.5"
+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.
+
+**Note: The chat template has been updated compared to the previous DeepSeek-V2-Chat version.**
+
+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-V2.5"
+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)
+```
+
+### Function calling
+
+Function calling allows the model to call external tools to enhance its capabilities.
+
+Here is an example:
+
+```python
+# Assume that `model` and `tokenizer` are loaded
+model.generation_config = GenerationConfig(do_sample=False, max_new_tokens=128, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id)
+
+tool_system_prompt = """You are a helpful Assistant.
+
+## Tools
+
+### Function
+
+You have the following functions available:
+
+- `get_current_weather`:
+```json
+{
+    "name": "get_current_weather",
+    "description": "Get the current weather in a given location",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "location": {
+                "type": "string",
+                "description": "The city and state, e.g. San Francisco, CA"
+            },
+            "unit": {
+                "type": "string",
+                "enum": [
+                    "celsius",
+                    "fahrenheit"
+                ]
+            }
+        },
+        "required": [
+            "location"
+        ]
+    }
+}
+```"""
+
+tool_call_messages = [{"role": "system", "content": tool_system_prompt}, {"role": "user", "content": "What's the weather like in Tokyo and Paris?"}]
+tool_call_inputs = tokenizer.apply_chat_template(tool_call_messages, add_generation_prompt=True, return_tensors="pt")
+tool_call_outputs = model.generate(tool_call_inputs.to(model.device))
+# Generated text: '<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>get_current_weather\n```json\n{"location": "Tokyo"}\n```<｜tool▁call▁end｜>\n<｜tool▁call▁begin｜>function<｜tool▁sep｜>get_current_weather\n```json\n{"location": "Paris"}\n```<｜tool▁call▁end｜><｜tool▁calls▁end｜><｜end▁of▁sentence｜>'
+
+# Mock response of calling `get_current_weather`
+tool_messages = [{"role": "tool", "content": '{"location": "Tokyo", "temperature": "10", "unit": null}'}, {"role": "tool", "content": '{"location": "Paris", "temperature": "22", "unit": null}'}]
+tool_inputs = tokenizer.apply_chat_template(tool_messages, add_generation_prompt=False, return_tensors="pt")[:, 1:]
+tool_inputs = torch.cat([tool_call_outputs, tool_inputs.to(model.device)], dim=1)
+tool_outputs = model.generate(tool_inputs)
+# Generated text: The current weather in Tokyo is 10 degrees, and in Paris, it is 22 degrees.<｜end▁of▁sentence｜>
+```
+
+### JSON output
+
+You can use JSON Output Mode to ensure the model generates a valid JSON object. To active this mode, a special instruction should be appended to your system prompt.
+
+```python
+# Assume that `model` and `tokenizer` are loaded
+model.generation_config = GenerationConfig(do_sample=False, max_new_tokens=128, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id)
+
+user_system_prompt = 'The user will provide some exam text. Please parse the "question" and "answer" and output them in JSON format.'
+json_system_prompt = f"""{user_system_prompt}
+
+## Response Format
+
+Reply with JSON object ONLY."""
+
+json_messages = [{"role": "system", "content": json_system_prompt}, {"role": "user", "content": "Which is the highest mountain in the world? Mount Everest."}]
+json_inputs = tokenizer.apply_chat_template(json_messages, add_generation_prompt=True, return_tensors="pt")
+json_outpus = model.generate(json_inputs.to(model.device))
+# Generated text: '```json\n{\n  "question": "Which is the highest mountain in the world?",\n  "answer": "Mount Everest."\n}\n```<｜end▁of▁sentence｜>'
+```
+
+### FIM completion
+
+In FIM (Fill In the Middle) completion, you can provide a prefix and an optional suffix, and the model will complete the content in between.
+
+```python
+# Assume that `model` and `tokenizer` are loaded
+model.generation_config = GenerationConfig(do_sample=False, max_new_tokens=128, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id)
+
+prefix = """def quick_sort(arr):
+    if len(arr) <= 1:
+        return arr
+    pivot = arr[0]
+    left = []
+    right = []
+"""
+
+suffix = """
+        if arr[i] < pivot:
+            left.append(arr[i])
+        else:
+            right.append(arr[i])
+    return quick_sort(left) + [pivot] + quick_sort(right)"""
+
+fim_prompt = f"<｜fim▁begin｜>{prefix}<｜fim▁hole｜>{suffix}<｜fim▁end｜>"
+fim_inputs = tokenizer(fim_prompt, add_special_tokens=True, return_tensors="pt").input_ids
+fim_outputs = model.generate(fim_inputs.to(model.device))
+# Generated text: "    for i in range(1, len(arr)):<｜end▁of▁sentence｜>"
+```
+
+## 3. License
+This code repository is licensed under the MIT License. The use of DeepSeek-V2 Base/Chat models is subject to [the Model License](LICENSE). DeepSeek-V2 series (including Base and Chat) supports commercial use.
+
+## 4. 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}
+}
+```
+
+## 5. Contact
+If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).