Instructions to use nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES with Transformers:

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

pipe = pipeline("image-text-to-text", model="nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES")
model = AutoModelForMultimodalLM.from_pretrained("nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES

SGLang

How to use nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES 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 "nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES" \
    --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": "nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES" \
        --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": "nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES with Docker Model Runner:
```
docker model run hf.co/nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Huihui-Qwen3.5-9B-abliterated-TIES

This is a merge of pre-trained language models created using a custom fork of mergekit with Qwen3.5 architecture support.

Merge Details

Merge Method

This model was merged using the TIES merge method using Qwen/Qwen3.5-9B-Base as a base.

Models Merged

The following models were included in the merge:

Mergekit Changes for Qwen3.5

Qwen3.5 uses a hybrid attention architecture (3:1 linear/full attention layers) that mergekit does not yet support upstream. The following changes were made to enable this merge:

New architecture definitions (qwen3_5.json, qwen3_5_text.json) - Defines tensor mappings for both Qwen3_5ForConditionalGeneration (VLM) and Qwen3_5ForCausalLM (text-only) architectures. The hybrid self_attn and linear_attn layer weights are marked as optional since they only appear on specific layers (full attention every 4th layer, linear attention on the rest).
Config key fallback (mergekit/common.py) - Added a fallback in get_config_value so that nested config keys like text_config.num_hidden_layers gracefully resolve to num_hidden_layers on text-only model configs. This enables cross-architecture merges between VLM and text-only Qwen3.5 variants.
Vision weight grafting - The SFT and ORPO fine-tuned models are text-only (Qwen3_5ForCausalLM) and lack the vision encoder. After the text merge, the vision encoder (model.visual.*) and MTP head (mtp.*) weights were grafted from the base VLM model (Qwen/Qwen3.5-9B-Base) to produce a complete VLM.

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: huihui-ai/Huihui-Qwen3.5-9B-abliterated
    parameters:
      weight: 0.4
      density: 0.6
  - model: nbeerbower/Huihui-Qwen3.5-9B-abliterated-Grimoire-SFT
    parameters:
      weight: 0.3
      density: 0.6
  - model: nbeerbower/Huihui-Qwen3.5-9B-abliterated-Grimoire-ORPO
    parameters:
      weight: 0.5
      density: 0.6
merge_method: ties
base_model: Qwen/Qwen3.5-9B-Base
parameters:
  normalize: true
  int8_mask: true
dtype: bfloat16