Instructions to use prithivMLmods/Qwen3.5-35B-A3B-abliterated-v2-MAX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen3.5-35B-A3B-abliterated-v2-MAX")
model = AutoModelForMultimodalLM.from_pretrained("prithivMLmods/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-abliterated-v2-MAX with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Qwen3.5-35B-A3B-abliterated-v2-MAX",
	filename="GGUF/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-abliterated-v2-MAX:BF16
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-abliterated-v2-MAX:BF16
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-abliterated-v2-MAX:BF16
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-abliterated-v2-MAX:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Qwen3.5-35B-A3B-abliterated-v2-MAX:BF16

Use Docker

docker model run hf.co/prithivMLmods/Qwen3.5-35B-A3B-abliterated-v2-MAX:BF16

LM Studio
Jan

vLLM

How to use prithivMLmods/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-abliterated-v2-MAX:BF16

SGLang

How to use prithivMLmods/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-abliterated-v2-MAX with Ollama:
```
ollama run hf.co/prithivMLmods/Qwen3.5-35B-A3B-abliterated-v2-MAX:BF16
```

Unsloth Studio

How to use prithivMLmods/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-abliterated-v2-MAX to start chatting

How to use prithivMLmods/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-abliterated-v2-MAX:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-abliterated-v2-MAX:BF16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use prithivMLmods/Qwen3.5-35B-A3B-abliterated-v2-MAX with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Qwen3.5-35B-A3B-abliterated-v2-MAX:BF16
```

Lemonade

How to use prithivMLmods/Qwen3.5-35B-A3B-abliterated-v2-MAX with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Qwen3.5-35B-A3B-abliterated-v2-MAX:BF16

Run and chat with the model

lemonade run user.Qwen3.5-35B-A3B-abliterated-v2-MAX-BF16

List all available models

lemonade list

Qwen3.5-35B-A3B-abliterated-v2-MAX

Qwen3.5-35B-A3B-abliterated-v2-MAX is an optimized release built on top of huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated. This version focuses on updated shard sizing, repository optimization, and compatibility improvements for the latest Transformers releases, while preserving the reasoning and instruction-following capabilities of the original model. The result is a powerful 35B parameter Mixture-of-Experts language model designed for efficient deployment, stable inference, and modern ecosystem integration.

This model is developed for research and learning purposes only. Any content generated by this model is used at the user's own risk. The authors and hosting platform disclaim any liability for outputs produced by this model. Users are responsible for ensuring safe, ethical, and lawful usage.

Compression for the Model

Qwen3.5-35B-A3B-abliterated-v2-MAX

Format	Description	Link
GGUF	Quantized GGUF format	https://huggingface.co/prithivMLmods/Qwen3.5-35B-A3B-abliterated-v2-MAX/tree/main/GGUF

Key Highlights

Latest Transformers Compatibility Re-sharded and optimized for improved compatibility with recent Transformers releases.
Optimized Model Sharding Updated shard structure for better storage handling, download reliability, and inference efficiency.
Stable Inference Pipeline Improved packaging for consistent loading and generation behavior across environments.
35B MoE Architecture (A3B) Built on Qwen3.5-35B-A3B, leveraging Mixture-of-Experts design for scalable reasoning capacity.
Improved Deployment Stability Designed for smoother inference across different hardware configurations and runtimes.
Preserved Model Behavior No changes to weights or architecture; behavior remains consistent with the base model lineage.

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-Qwen3.5-35B-A3B-abliterated.

Quick Start with Transformers

pip install transformers==5.5.0
# or
pip install git+https://github.com/huggingface/transformers.git

from transformers import Qwen3_5MoeForConditionalGeneration, AutoProcessor
import torch

model = Qwen3_5MoeForConditionalGeneration.from_pretrained(
    "prithivMLmods/Qwen3.5-35B-A3B-abliterated-v2-MAX",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/Qwen3.5-35B-A3B-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 large-scale MoE behavior and inference characteristics.
Red-Teaming & Evaluation Testing robustness across complex and adversarial prompts.
High-Performance Deployment Running large MoE models on optimized multi-GPU setups.
Research Prototyping Experimentation with scalable transformer architectures and deployment workflows.

Limitations & Risks

Important Note: This model inherits the behavior and limitations of its base model.

Output Variability Responses may vary depending on sampling settings and prompt structure.
Resource Requirements A 35B MoE model requires significant GPU memory or optimized inference strategies such as quantization or tensor parallelism.
Deployment Constraints Performance depends heavily on hardware configuration and runtime optimization.
General Model Limitations May produce incorrect, incomplete, or inconsistent outputs in complex scenarios.