Instructions to use deepseek-ai/DeepSeek-V3.2-Exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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")
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
```

Question about long-context evaluation in DeepSeek-V3.2-Exp

#15

by fcMpKYz6Avp5QK - opened Sep 30, 2025

Discussion

fcMpKYz6Avp5QK

Sep 30, 2025

According to the release notes, DeepSeek-V3.2-Exp improves cost efficiency for long contexts without performance regression on standard benchmarks.

Looking at the cost-vs-context-length chart, I noticed an interesting pattern: around the 4k-8k token range, there's a noticeable change in the linearity of the graph, suggesting some form of attention optimization kicks in around this point. Actually, below this range, V3.2-Exp appears slightly more costly than the previous version, which makes the long-context improvements even more intriguing.

However, since common benchmarks like MMLU and GSM8K primarily evaluate short-context tasks, I'm curious if there are any specific long-context benchmark results available to validate the actual long-context capabilities?

Could you share if evaluations were conducted on long-context benchmarks such as LongBench or L-Eval, and if so, what were the results? This would help understand whether the observed attention optimization translates to measurable performance gains in long-context tasks.

Note: This question was refined with the assistance of DeepSeek-V3.2-Exp itself to improve clarity and precision.

ghostplant

Oct 1, 2025

•

edited Oct 1, 2025

Where is modeling_deepseek.py?

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment