Instructions to use YOYO-AI/Qwen2.5-32B-YOYO-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YOYO-AI/Qwen2.5-32B-YOYO-V2 with Transformers:

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

pipe = pipeline("text-generation", model="YOYO-AI/Qwen2.5-32B-YOYO-V2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("YOYO-AI/Qwen2.5-32B-YOYO-V2")
model = AutoModelForCausalLM.from_pretrained("YOYO-AI/Qwen2.5-32B-YOYO-V2")
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

vLLM

How to use YOYO-AI/Qwen2.5-32B-YOYO-V2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YOYO-AI/Qwen2.5-32B-YOYO-V2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YOYO-AI/Qwen2.5-32B-YOYO-V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/YOYO-AI/Qwen2.5-32B-YOYO-V2

SGLang

How to use YOYO-AI/Qwen2.5-32B-YOYO-V2 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 "YOYO-AI/Qwen2.5-32B-YOYO-V2" \
    --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": "YOYO-AI/Qwen2.5-32B-YOYO-V2",
		"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 "YOYO-AI/Qwen2.5-32B-YOYO-V2" \
        --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": "YOYO-AI/Qwen2.5-32B-YOYO-V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use YOYO-AI/Qwen2.5-32B-YOYO-V2 with Docker Model Runner:
```
docker model run hf.co/YOYO-AI/Qwen2.5-32B-YOYO-V2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen2.5-32B-YOYO-V2

The YOYO Second Generation 32B Model is Released!

Highlights:

1. Using the Karcher merging method.

2. Integrating high-performance 32B models from the open-source community.

First stage:

Make a code model:

models:
  - model: Qwen/Qwen2.5-Coder-32B-instruct
    parameters:
      density: 1 
      weight: 1
      lambda: 0.9
merge_method: della
base_model: Qwen/Qwen2.5-Coder-32B
parameters:
  density: 1
  weight: 1
  lambda: 0.9
  normalize: true
  int8_mask: true
dtype: bfloat16
name: YOYO-AI/Qwen2.5-Coder-32B-YOYO

Second stage:

Make an instruction model:

models:
  - model: YOYO-AI/Qwen2.5-Coder-32B-YOYO
  - model: Qwen/QwQ-32B
  - model: Skywork/Skywork-OR1-32B-Preview
  - model: deepcogito/cogito-v1-preview-qwen-32B
  - model: qihoo360/Light-R1-32B
  - model: AXCXEPT/EZO-Qwen2.5-32B-Instruct
  - model: fblgit/TheBeagle-v2beta-32B-MGS
  - model: tanliboy/lambda-qwen2.5-32b-dpo-test
  - model: Qwen/Qwen2.5-32B-Instruct
merge_method: karcher
base_model: Qwen/Qwen2.5-32B-Instruct
parameters:
  max_iter: 1000
normalize: true
int8_mask: true
tokenizer_source: base
dtype: float16
name: YOYO-AI/Qwen2.5-32B-YOYO-karcher

Third stage:

Make a base model:

models:
  - model: EVA-UNIT-01/EVA-Qwen2.5-32B-v0.2
  - model: Azure99/Blossom-V6-32B
  - model: arcee-ai/Virtuoso-Medium-v2
merge_method: karcher
base_model: Qwen/Qwen2.5-32B
parameters:
  max_iter: 1000
normalize: true
int8_mask: true
tokenizer_source: base
dtype: float16
name: YOYO-AI/Qwen2.5-32B-YOYO-karcher-base

Final stage:

models:
  - model: YOYO-AI/Qwen2.5-32B-YOYO-karcher
    parameters:
      density: 1
      weight: 1
      lambda: 0.9
merge_method: della
base_model: YOYO-AI/Qwen2.5-32B-YOYO-karcher-base
parameters:
  density: 1
  weight: 1
  lambda: 0.9
  normalize: true
  int8_mask: true
dtype: bfloat16
tokenizer_source: base
name: YOYO-AI/Qwen2.5-32B-YOYO-V2