Instructions to use prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX")
model = AutoModelForImageTextToText.from_pretrained("prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX")
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]:]))

llama-cpp-python

How to use prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX",
	filename="GGUF/Qwen3.5-9B-abliterated-v2-MAX.BF16.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX:BF16
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX:BF16
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX:BF16
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX:BF16

Use Docker

docker model run hf.co/prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX:BF16

LM Studio
Jan

vLLM

How to use prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX",
		"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/prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX:BF16

SGLang

How to use prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX 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 "prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX" \
    --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": "prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX",
		"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 "prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX" \
        --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": "prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX",
		"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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX with Ollama:
```
ollama run hf.co/prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX:BF16
```

Unsloth Studio

How to use prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX to start chatting

How to use prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX:BF16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX:BF16

Run Hermes

hermes

Docker Model Runner
How to use prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX:BF16
```

Lemonade

How to use prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX:BF16

Run and chat with the model

lemonade run user.Qwen3.5-9B-abliterated-v2-MAX-BF16

List all available models

lemonade list

Qwen3.5-9B-abliterated-v2-MAX

Qwen3.5-9B-abliterated-v2-MAX is an optimized release built on top of huihui-ai/Huihui-Qwen3.5-9B-abliterated. This version focuses on improved model sharding, packaging consistency, and compatibility with modern Transformers and inference stacks, while preserving the reasoning and instruction-following capabilities of the base model. The result is a highly capable 9B parameter language model designed for efficient deployment, stable inference, and research-oriented experimentation.

This model is intended strictly for research and learning purposes only. Any outputs generated by this model are the sole responsibility of the user. The authors and hosting platform disclaim all liability for model-generated content. Users must ensure safe, ethical, and lawful usage.

Compression for the Model

Qwen3.5-9B-abliterated-v2-MAX

Format	Description	Link
GGUF	Quantized GGUF format	https://huggingface.co/prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX/tree/main/GGUF
NVFP4	NVFP4 compressed model	https://huggingface.co/prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX-NVFP4
FP8	FP8 compressed model	https://huggingface.co/prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX-FP8

Base Model Signatures:

This model has been re-sharded and optimized for the latest Transformers version from the base model: https://huggingface.co/huihui-ai/Huihui-Qwen3.5-9B-abliterated

Key Highlights

Optimized Packaging & Sharding Improved repository structure for smoother downloads, loading, and deployment across environments.
Stable Transformers Compatibility Updated layout for better compatibility with modern Transformers versions and inference pipelines.
9B Parameter Architecture Built on Qwen3.5-9B, balancing efficiency and capability for local and research use.
Efficient Deployment Design Designed for lightweight inference, experimentation, and scalable integration.
Preserved Model Behavior No changes to weights or core architecture; performance remains consistent with the original base model lineage.
Improved Reliability in Loading Reduced friction in model initialization and multi-device inference setups.

Quick Start with Transformers

pip install transformers==5.4.0
# or
pip install git+https://github.com/huggingface/transformers.git

from transformers import Qwen3_5ForConditionalGeneration, AutoProcessor
import torch

model = Qwen3_5ForConditionalGeneration.from_pretrained(
    "prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/Qwen3.5-9B-abliterated-v2-MAX"
)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Explain how transformer models work in simple terms."}
        ],
    }
]

text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)

inputs = processor(
    text=[text],
    padding=True,
    return_tensors="pt"
).to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=256)

generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]

output_text = processor.batch_decode(
    generated_ids_trimmed,
    skip_special_tokens=True,
    clean_up_tokenization_spaces=False
)

print(output_text)

Intended Use

Multimodal and Language Research Studying behavior of compact 9B-scale transformer models under different inference settings.
Red-Teaming & Evaluation Testing robustness across adversarial prompts and edge-case inputs.
Efficient Local Deployment Running lightweight yet capable models on consumer GPUs or optimized cloud setups.
Research Prototyping Exploring model behavior, alignment, and inference optimization techniques.

Limitations & Risks

Important Note: This model inherits behavior from its base model with minimal modification.

Output Variability Responses may vary depending on sampling strategy and prompt formulation.
Resource Dependency While efficient, GPU acceleration is recommended for optimal performance.
No Architectural Changes Improvements are limited to packaging and compatibility, not core model capabilities.
General Model Limitations May still produce incorrect, incomplete, or inconsistent outputs in complex scenarios.