Huihui-Qwen3-VL-8B-Thinking-abliterated-AWQ

Model Highlights

Datasets:

• Salesforce/wikitext
• lmms-lab/COCO-Caption2017

Base Model:

• huihui-ai/Huihui-Qwen3-VL-8B-Thinking-abliterated

Use with VLLM

1. Download models at first by using hf
   hf download nicklas373/Huihui-Qwen3-VL-8B-Thinking-abliterated-AWQ-8-bit
2. Copy hash for snapshots directory, then use it for chat templates and tool call parser ex: /home/xxx/.cache/huggingface/hub/models--nicklas373--Huihui-Qwen3-VL-8B-Thinking-abliterated-AWQ-8-bit/snapshots/HASH_CODE/xxx
3. Run models with this command

huihui-ai/Huihui-Qwen3-VL-8B-Thinking-abliterated

ollama

Chat with Image

from transformers import Qwen3VLForConditionalGeneration, AutoProcessor, BitsAndBytesConfig
import os
import torch

cpu_count = os.cpu_count()
print(f"Number of CPU cores in the system: {cpu_count}")
half_cpu_count = cpu_count // 2
os.environ["MKL_NUM_THREADS"] = str(half_cpu_count)
os.environ["OMP_NUM_THREADS"] = str(half_cpu_count)
torch.set_num_threads(half_cpu_count)

MODEL_ID = "huihui-ai/Huihui-Qwen3-VL-8B-Thinking-abliterated"

# default: Load the model on the available device(s)
model = Qwen3VLForConditionalGeneration.from_pretrained(
    MODEL_ID, 
    device_map="auto", 
    trust_remote_code=True,
    dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
)
# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
# model = Qwen3VLForConditionalGeneration.from_pretrained(
#     "Qwen/Qwen3-VL-235B-A22B-Instruct",
#     dtype=torch.bfloat16,
#     attn_implementation="flash_attention_2",
#     device_map="auto",
# )

processor = AutoProcessor.from_pretrained(MODEL_ID)


image_path = "/png/cars.jpg"

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image", "image": f"{image_path}",
            },
            {"type": "text", "text": "Describe this image."},
        ],
    }
]

# Preparation for inference
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
).to(model.device)

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
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)

Usage Warnings

• Risk of Sensitive or Controversial Outputs: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.
• Not Suitable for All Audiences: Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security.
• Legal and Ethical Responsibilities: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences.
• Research and Experimental Use: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications.
• Monitoring and Review Recommendations: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content.
• No Default Safety Guarantees: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.

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