Instructions to use deepseek-ai/DeepSeek-V3.2-Exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepseek-ai/DeepSeek-V3.2-Exp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-V3.2-Exp", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V3.2-Exp") model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-V3.2-Exp", 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]:])) - Inference
- HuggingChat
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use deepseek-ai/DeepSeek-V3.2-Exp with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "deepseek-ai/DeepSeek-V3.2-Exp" # 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-V3.2-Exp", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/deepseek-ai/DeepSeek-V3.2-Exp
- SGLang
How to use deepseek-ai/DeepSeek-V3.2-Exp 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-V3.2-Exp" \ --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-V3.2-Exp", "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-V3.2-Exp" \ --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-V3.2-Exp", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use deepseek-ai/DeepSeek-V3.2-Exp with Docker Model Runner:
docker model run hf.co/deepseek-ai/DeepSeek-V3.2-Exp
能不能一直保留旧版的deepseek v3.1的API接口?
#10
by lixin4sky - opened
旧版v3.1在很多agent场景下,效果要比现在的v3.2好一些,请问可以一直保留v3.1的API接口吗?
我看到公告是保留到10.15,但是10.15能适配好v3.2吗?
建议像google、openai一样保留旧版接口,这样方便用户选择
我推测是DS资源比较紧,所以推理层面就放弃维护旧版本接口,只保留一个最新的。算力资源主要都在模型训练上。
都是开源的,如果需要3.1可以云厂商的MaaS
都是开源模型,各种供应商逛一圈大多数都有旧版保留,或者直接openrouter直接耍
我估计15号之前会推出3.2正式版或者新版本,至于fc遵循和prompt适配我估计 ds不会管这些东西。ds本身自家机器就少,ds官方api的上下文以前都是阉割的,现在不知道了。如果你觉得有问题可以用火山引擎阿里云之类的,他们都是长期提供版本支持的,至少半年一年不会下线。
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