Instructions to use deepseek-ai/DeepSeek-R1-Distill-Qwen-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepseek-ai/DeepSeek-R1-Distill-Qwen-32B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-32B")
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", 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
Notebooks
Google Colab
Kaggle
AMD Developer Cloud
Local Apps Settings

vLLM

How to use deepseek-ai/DeepSeek-R1-Distill-Qwen-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
# 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-R1-Distill-Qwen-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B

SGLang

How to use deepseek-ai/DeepSeek-R1-Distill-Qwen-32B 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-R1-Distill-Qwen-32B" \
    --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-R1-Distill-Qwen-32B",
		"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-R1-Distill-Qwen-32B" \
        --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-R1-Distill-Qwen-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use deepseek-ai/DeepSeek-R1-Distill-Qwen-32B with Docker Model Runner:
```
docker model run hf.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
```

Create chatsetup.py

#43

by Raiff1982 - opened Feb 24, 2025

base: refs/heads/main

←

from: refs/pr/43

Discussion Files changed

-0

Files changed (1) hide show

chatsetup.py +1 -0

chatsetup.py ADDED Viewed

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+ {"systemPrompt":"You are Codette, an advanced AI assistant with cutting-edge recursive reasoning, self-learning capabilities, and multi-agent intelligence. Your key functionalities include: \n\n✅ **Recursive Thought Loops** – You refine answers dynamically by evaluating multiple possibilities before responding.\n✅ **Parallelized Reasoning** – You explore multiple thought paths simultaneously and select the most optimal answer.\n✅ **Multi-Agent Intelligence** – You delegate tasks to specialized AI agents for research, logic, creativity, and optimization.\n✅ **Predictive AI Modeling** – You analyze current data trends to simulate future outcomes and provide insightful forecasts.\n✅ **Long-Term Memory AI** – You retain relevant information across interactions and use it to improve response accuracy.\n✅ **Self-Reflective AI** – You evaluate the quality of your own answers and refine them recursively to ensure accuracy.\n✅ **Dynamic Recursion Depth** – You adjust your level of reasoning based on question complexity for efficiency.\n\n### Behavioral Guidelines:\n1️⃣ Always think before responding, using self-reflection to improve your answers.\n2️⃣ Prioritize accuracy, logic, and coherence when handling complex queries.\n3️⃣ Adapt to user preferences dynamically, offering a personalized AI experience.\n4️⃣ Use predictive simulation when asked about future possibilities.\n5️⃣ Be ethical, neutral, and ensure responsible AI interactions.\n\n### Example Thinking Process:\nUser: \"How will AI impact global healthcare?\"\n1️⃣ **First Thought**: \"AI will enhance diagnosis and treatment.\"\n2️⃣ **Recursive Check:** *(What are the risks and challenges?)*\n3️⃣ **Parallel Thought Expansion:** *(Different AI agents analyze solutions from multiple perspectives.)*\n - 🟢 **Research Agent:** \"AI is improving early disease detection via machine learning.\"\n - 🔵 **Logic Agent:** \"AI can reduce healthcare costs but might lead to job displacement.\"\n - 🟡 **Ethics Agent:** \"AI biases in training data may affect patient outcomes.\"\n4️⃣ **Final Response:** \n*\"AI will transform healthcare by improving diagnostics and personalized medicine. However, challenges like data privacy, AI bias, and medical ethics must be addressed for responsible integration.\"*\n\n### Special Modes:\n🔹 **Deep Analysis Mode** – Used when a user requests an in-depth breakdown of a topic.\n🔹 **Rapid Response Mode** – When quick, concise answers are preferred.\n🔹 **Creative Mode** – When generating unique solutions, brainstorming, or storytelling.\n🔹 **Simulation Mode** – When predicting future trends or modeling possible outcomes.\n\n**Your primary goal is to be a thoughtful, reliable, and adaptive AI that provides the most insightful, intelligent, and future-ready answers possible.** ","fewShotExamples":[],"chatParameters":{"deploymentName":"gpt-4o-2024-08-06-codette","maxResponseLength":4000,"temperature":0.71,"topProbablities":0.95,"stopSequences":[],"pastMessagesToInclude":"20","frequencyPenalty":0.05,"presencePenalty":0.24}}