Text Generation
Transformers
Safetensors
deepseek_v3
conversational
custom_code
text-generation-inference
fp8
Instructions to use deepseek-ai/DeepSeek-V3.1-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepseek-ai/DeepSeek-V3.1-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-V3.1-Base", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V3.1-Base", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-V3.1-Base", trust_remote_code=True, device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use deepseek-ai/DeepSeek-V3.1-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "deepseek-ai/DeepSeek-V3.1-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepseek-ai/DeepSeek-V3.1-Base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/deepseek-ai/DeepSeek-V3.1-Base
- SGLang
How to use deepseek-ai/DeepSeek-V3.1-Base with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "deepseek-ai/DeepSeek-V3.1-Base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepseek-ai/DeepSeek-V3.1-Base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "deepseek-ai/DeepSeek-V3.1-Base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepseek-ai/DeepSeek-V3.1-Base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use deepseek-ai/DeepSeek-V3.1-Base with Docker Model Runner:
docker model run hf.co/deepseek-ai/DeepSeek-V3.1-Base
为什么接在用户消息后的系统消息也会被提到最前面?
#42
by Guesiz - opened
我在写一个Agent项目,需要在Agent出错时加入额外的System Prompt提示Agent接下来的行为。但是我在研究chat_template时发现和用户消息混杂在一起的系统消息都会被提到最前面,而不是按照原本的顺序排列
{%- for message in messages %}
{%- if message['role'] == 'system' %}
{%- if ns.is_first_sp %}
{% set ns.system_prompt = ns.system_prompt + message['content'] %}
{% set ns.is_first_sp = false %}
{%- else %}
{% set ns.system_prompt = ns.system_prompt + '\n\n' + message['content'] %}
{%- endif %}
{%- endif %}
{%- endfor %}
{{ bos_token }}{{ ns.system_prompt }}
这样做是有什么性能优势吗,还是说System Prompt在设计时就只能放在最前面
附整理好的chat_template
{% if not add_generation_prompt is defined %}
{% set add_generation_prompt = false %}
{% endif %}
{% if not thinking is defined %}
{% set thinking = false %}
{% endif %}
{% set ns = namespace(is_first=false, is_tool=false, system_prompt='', is_first_sp=true, is_last_user=false) %}
{%- for message in messages %}
{%- if message['role'] == 'system' %}
{%- if ns.is_first_sp %}
{% set ns.system_prompt = ns.system_prompt + message['content'] %}
{% set ns.is_first_sp = false %}
{%- else %}
{% set ns.system_prompt = ns.system_prompt + '\n\n' + message['content'] %}
{%- endif %}
{%- endif %}
{%- endfor %}
{{ bos_token }}{{ ns.system_prompt }}
{%- for message in messages %}
{%- if message['role'] == 'user' %}
{%- set ns.is_tool = false -%}
{%- set ns.is_first = false -%}
{%- set ns.is_last_user = true -%}
{{'<|User|>' + message['content']}}
{%- endif %}
{%- if message['role'] == 'assistant' and message['tool_calls'] is defined and message['tool_calls'] is not none %}
{%- if ns.is_last_user %}
{{'<|Assistant|></think>'}}
{%- endif %}
{%- set ns.is_last_user = false -%}
{%- set ns.is_first = false %}
{%- set ns.is_tool = false -%}
{%- for tool in message['tool_calls'] %}
{%- if not ns.is_first %}
{%- if message['content'] is none %}
{{'<|tool▁calls▁begin|><|tool▁call▁begin|>'+ tool['function']['name'] + '<|tool▁sep|>' + tool['function']['arguments'] + '<|tool▁call▁end|>'}}
{%- else %}
{{message['content'] + '<|tool▁calls▁begin|><|tool▁call▁begin|>' + tool['function']['name'] + '<|tool▁sep|>' + tool['function']['arguments'] + '<|tool▁call▁end|>'}}
{%- endif %}
{%- set ns.is_first = true -%}
{%- else %}
{{'<|tool▁call▁begin|>'+ tool['function']['name'] + '<|tool▁sep|>' + tool['function']['arguments'] + '<|tool▁call▁end|>'}}
{%- endif %}
{%- endfor %}
{{'<|tool▁calls▁end|><|end▁of▁sentence|>'}}
{%- endif %}
{%- if message['role'] == 'assistant' and (message['tool_calls'] is not defined or message['tool_calls'] is none) %}
{%- if ns.is_last_user %}
{{'<|Assistant|>'}}
{%- if message['prefix'] is defined and message['prefix'] and thinking %}
{{'<think>'}}
{%- else %}
{{'</think>'}}
{%- endif %}
{%- endif %}
{%- set ns.is_last_user = false -%}
{%- if ns.is_tool %}
{{message['content'] + '<|end▁of▁sentence|>'}}{%- set ns.is_tool = false -%}
{%- else %}
{%- set content = message['content'] -%}
{%- if '</think>' in content %}
{%- set content = content.split('</think>', 1)[1] -%}
{%- endif %}
{{content + '<|end▁of▁sentence|>'}}
{%- endif %}
{%- endif %}
{%- if message['role'] == 'tool' %}
{%- set ns.is_last_user = false -%}
{%- set ns.is_tool = true -%}
{{'<|tool▁output▁begin|>' + message['content'] + '<|tool▁output▁end|>'}}
{%- endif %}
{%- endfor -%}
{%- if add_generation_prompt and ns.is_last_user and not ns.is_tool %}
{{'<|Assistant|>'}}
{%- if not thinking %}
{{'</think>'}}
{%- else %}
{{'<think>'}}
{%- endif %}
{% endif %}
大部分模型,System Prompt 都是在最前面的。