Instructions to use trashpanda-org/QwQ-32B-Snowdrop-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use trashpanda-org/QwQ-32B-Snowdrop-v0 with Transformers:

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

pipe = pipeline("text-generation", model="trashpanda-org/QwQ-32B-Snowdrop-v0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("trashpanda-org/QwQ-32B-Snowdrop-v0")
model = AutoModelForCausalLM.from_pretrained("trashpanda-org/QwQ-32B-Snowdrop-v0")
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
Local Apps Settings

vLLM

How to use trashpanda-org/QwQ-32B-Snowdrop-v0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "trashpanda-org/QwQ-32B-Snowdrop-v0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trashpanda-org/QwQ-32B-Snowdrop-v0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/trashpanda-org/QwQ-32B-Snowdrop-v0

SGLang

How to use trashpanda-org/QwQ-32B-Snowdrop-v0 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 "trashpanda-org/QwQ-32B-Snowdrop-v0" \
    --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": "trashpanda-org/QwQ-32B-Snowdrop-v0",
		"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 "trashpanda-org/QwQ-32B-Snowdrop-v0" \
        --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": "trashpanda-org/QwQ-32B-Snowdrop-v0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use trashpanda-org/QwQ-32B-Snowdrop-v0 with Docker Model Runner:
```
docker model run hf.co/trashpanda-org/QwQ-32B-Snowdrop-v0
```

New line after think tag

by djuna - opened Mar 11, 2025

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

-3

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -9,7 +9,6 @@ tags:
 - mergekit
 - mergekitty
 - merge
 ---
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/675a77cf99ca23af9daacccc/Tdn0PJBFnG3J6UcjO9G94.png)
@@ -42,7 +41,8 @@ Style Preference: Encourage the usage of a Japanese light novel writing style.
 Deciding to fixate on that, my reasoning starter is:
 ```
-<think>Okay, in this scenario, before responding I need to consider the writing style referenced in the prompt, which is
 ```
 What this did for me, at least during testing is that it gave the reasoning a structure to follow across rerolls, seeking out that part of the prompt consistently.
@@ -184,4 +184,4 @@ tokenizer_source: Qwen/Qwen2.5-32B-Instruct
 dtype: bfloat16
-```

 - mergekit
 - mergekitty
 - merge
 ---
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/675a77cf99ca23af9daacccc/Tdn0PJBFnG3J6UcjO9G94.png)
 Deciding to fixate on that, my reasoning starter is:
 ```
+<think>
+Okay, in this scenario, before responding I need to consider the writing style referenced in the prompt, which is
 ```
 What this did for me, at least during testing is that it gave the reasoning a structure to follow across rerolls, seeking out that part of the prompt consistently.
 dtype: bfloat16
+```