Instructions to use DarkKitsune/qwen3.5-9b-qworus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DarkKitsune/qwen3.5-9b-qworus with Transformers:

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

pipe = pipeline("text-generation", model="DarkKitsune/qwen3.5-9b-qworus")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("DarkKitsune/qwen3.5-9b-qworus")
model = AutoModelForMultimodalLM.from_pretrained("DarkKitsune/qwen3.5-9b-qworus")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DarkKitsune/qwen3.5-9b-qworus with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DarkKitsune/qwen3.5-9b-qworus"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DarkKitsune/qwen3.5-9b-qworus",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DarkKitsune/qwen3.5-9b-qworus

SGLang

How to use DarkKitsune/qwen3.5-9b-qworus 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 "DarkKitsune/qwen3.5-9b-qworus" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DarkKitsune/qwen3.5-9b-qworus",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "DarkKitsune/qwen3.5-9b-qworus" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DarkKitsune/qwen3.5-9b-qworus",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DarkKitsune/qwen3.5-9b-qworus with Docker Model Runner:
```
docker model run hf.co/DarkKitsune/qwen3.5-9b-qworus
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

qwen3.5-9b-qworus

60/40 DARE-TIES of empero-ai/Qwythos-9B-v2 and PeppX/Ornith-1.0-9B-Uncensored. This merge exists because I find Qwythos to be excellent for its genuine stability and ability to bug fix based on stderr output, but Ornith is better in its ability to set up a well-organized project with scalability in mind. So my goal is to cleanly combine the abilities of both.

Both source models are decensored, so this merge is decensored by default as well. It may inherit Qwythos' 1M context support due to using it as the base model, but I don't have the resources to actually test 1M tokens. The MTP head is also most likely not retained.

This model is for testing, and not intended to be used for any serious projects.

Mergekit

The following YAML configuration was used to produce this model:

merge_method: dare_ties
base_model: empero-ai/Qwythos-9B-v2

models:
  - model: empero-ai/Qwythos-9B-v2
    parameters:
      weight: 0.6
      density: 0.53

  - model: PeppX/Ornith-1.0-9B-Uncensored
    parameters:
      weight: 0.4
      density: 0.53

parameters:
  normalize: true
  int8_mask: true

dtype: bfloat16

Downloads last month: -

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for DarkKitsune/qwen3.5-9b-qworus

PeppX/Ornith-1.0-9B-Uncensored

empero-ai/Qwythos-9B-v2

Merge model

this model

Quantizations

1 model