prithivMLmods

Qwen3-VL-8B-Heretic-Stable

README

License: apache-2.0

Evaluation [Self Reported]

Table with columns: Metric, Result
Metric	Result
Refusal Rate	N/A
Test Setup	N/A
Inference Type	text-generation + vision-language
Dataset	N/A

Note: This release does not introduce new benchmark results and primarily focuses on repackaging, sharding updates, and Transformers compatibility improvements over the base model.

Key Highlights

Latest Transformers Compatibility Re-sharded and optimized for compatibility with recent Transformers releases.
Optimized Model Sharding Updated shard structure for improved download reliability, storage handling, and inference efficiency.
Stable Multimodal Pipeline Ensures consistent behavior across image-text inputs using the original Qwen3-VL architecture.
8B Vision-Language Architecture Built on Qwen3-VL-8B-Instruct, supporting strong image understanding and multimodal reasoning.
Improved Deployment Stability Designed for smoother loading and more predictable inference across environments.
Preserved Model Behavior No changes to core weights or architecture; behavior remains aligned 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-VL-8B-Instruct-abliterated

Quick Start with Transformers

bash
pip install transformers==5.9.0
# or
pip install git+https://github.com/huggingface/transformers.git

python
from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch

model = Qwen3VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/Qwen3-VL-8B-Heretic-Stable",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/Qwen3-VL-8B-Heretic-Stable"
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {
                "type": "text",
                "text": "Describe this image in detail."
            },
        ],
    }
]

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

image_inputs, video_inputs = process_vision_info(messages)

inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    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 Research Studying vision-language reasoning and model behavior across image-text inputs.
Dataset Enrichment Generating captions and structured descriptions for visual datasets.
Evaluation & Benchmarking Testing multimodal performance under varied prompts and image complexity.
Application Prototyping Building vision-language tools for analysis, captioning, and reasoning workflows.

Limitations & Risks

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

Output Variability Responses may vary depending on image quality, prompt design, and sampling parameters.
Multimodal Constraints Performance depends on resolution, context length, and input complexity.
Deployment Requirements Requires GPU acceleration for efficient inference, especially for high-resolution inputs.
General Model Limitations May still produce inaccurate or incomplete outputs in complex scenarios.

Available on FriendliAI

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Model Details

Model Provider

prithivMLmods

Model Tree

Base

prithivMLmods/Qwen3-VL-8B-Instruct-Unredacted-MAX

Fine-tuned

this model

Input Modalities

Text

Image

Output Modalities