DeepSeek-V2.5 将聊天和编码功能合二为一，现已开源。 针对写作、编码和人性化偏好进行了增强。 通过网络和 API 提供。
今天，我们成功合并了 DeepSeek-V2-Chat 和 DeepSeek-Coder-V2 模型，正式发布了DeepSeek-V2.5。
DeepSeek-V2.5 保留了Chat模型的一般对话能力和Coder模型的编码优势，同时更符合人类的偏好。 此外，DeepSeek-V2.5 在编写任务和指令跟踪方面也有了显著的改进。
DeepSeek-V2.5 现在已经完全可以在Web和API平台上使用。 API保持向后兼容，允许用户通过 deepseek-coder 或 deepseek-chat 访问新模型。 函数调用、FIM 补全和 JSON 输出功能保持不变。 一体化的 DeepSeek-V2.5 提供了更简单、更智能、更高效的用户体验。
| Metric | DeepSeek-V2-0628 | DeepSeek-Coder-V2-0724 | DeepSeek-V2.5 |
|---|---|---|---|
| AlpacaEval 2.0 | 46.6 | 44.5 | 50.5 |
| ArenaHard | 68.3 | 66.3 | 76.2 |
| AlignBench | 7.88 | 7.91 | 8.04 |
| MT-Bench | 8.85 | 8.91 | 9.02 |
| HumanEval python | 84.5 | 87.2 | 89 |
| HumanEval Multi | 73.8 | 74.8 | 73.8 |
| LiveCodeBench(01-09) | 36.6 | 39.7 | 41.8 |
| Aider | 69.9 | 72.9 | 72.2 |
| SWE-verified | N/A | 19 | 16.8 |
| DS-FIM-Eval | N/A | 73.2 | 78.3 |
| DS-Arena-Code | N/A | 49.5 | 63.1 |
如何在本地运行
要利用BF16格式的DeepSeek-V2.5进行推理，需要80GB*8个GPU。 利用Huggingface的Transformers进行推理
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)完整的聊天模板可在 huggingface 模型仓库中的 tokenizer_config.json 中找到。
注：与之前的 DeepSeek-V2-Chat 版本相比，聊天模板已经更新。
聊天模板示例如下：
<｜begin▁of▁sentence｜><｜User｜>{user_message_1}<｜Assistant｜>{assistant_message_1}<｜end▁of▁sentence｜><｜User｜>{user_message_2}<｜Assistant｜>您还可以添加一条可选的系统信息：
<｜begin▁of▁sentence｜>{system_message}<｜User｜>{user_message_1}<｜Assistant｜>{assistant_message_1}<｜end▁of▁sentence｜><｜User｜>{user_message_2}<｜Assistant｜>使用 vLLM 进行推理（推荐）
要利用 vLLM 进行模型推理，请将此 Pull Request 合并到您的 vLLM 代码库中：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)函数调用
函数调用允许模型调用外部工具来增强其功能。
下面是一个例子：
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 输出
您可以使用 JSON 输出模式来确保模型生成有效的 JSON 对象。 要激活此模式，应在系统提示中添加一条特殊指令。
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 补全
在 FIM（中间填充）补全中，您可以提供一个前缀和一个可选的后缀，模型将完成中间的内容。
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｜>"