131 lines
3.9 KiB
Markdown
131 lines
3.9 KiB
Markdown
---
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license: apache-2.0
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base_model:
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- Qwen/Qwen2.5-VL-3B-Instruct
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tags:
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- text-generation-inference
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- uncensored
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- abliterated
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- unfiltered
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- unredacted
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- vllm
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- pytorch
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- BF16
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- max
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- legal
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language:
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- en
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pipeline_tag: image-text-to-text
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library_name: transformers
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---
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# **Qwen2.5-VL-3B-Instruct-Unredacted-MAX**
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> **Qwen2.5-VL-3B-Instruct-Unredacted-MAX** is an optimized release built on top of **huihui-ai/Qwen2.5-VL-3B-Instruct-abliterated**. This version focuses on **improved model packaging, updated compatibility with modern Transformers pipelines, and stable multimodal inference behavior**, while preserving the core vision-language reasoning capabilities of the original architecture. The result is a compact **3B vision-language model** designed for efficient deployment, research experimentation, and multimodal application development.
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## Key Highlights
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* **Optimized Release Packaging**
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Streamlined repository structure for smoother loading, inference, and deployment workflows.
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* **Modern Transformers Compatibility**
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Updated to ensure stable integration with recent Hugging Face Transformers versions.
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* **3B Vision-Language Architecture**
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Built on **Qwen2.5-VL-3B-Instruct**, balancing multimodal capability with lightweight deployment requirements.
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* **Stable Multimodal Inference**
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Designed for consistent performance across image-text reasoning tasks.
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* **Efficient Caption Generation**
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Produces structured, descriptive outputs suitable for annotation and dataset building.
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* **Dynamic Resolution Support**
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Retains native handling of varying image resolutions and aspect ratios.
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---
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## Base Model Signatures:
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This model has been re-sharded and optimized for the latest Transformers version from the base model:
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https://huggingface.co/huihui-ai/Qwen2.5-VL-3B-Instruct-abliterated
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---
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## Quick Start with Transformers
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```python id="vl3b_code"
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
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from qwen_vl_utils import process_vision_info
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import torch
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model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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"prithivMLmods/Qwen2.5-VL-3B-Instruct-Unredacted-MAX",
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torch_dtype="auto",
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device_map="auto"
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)
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processor = AutoProcessor.from_pretrained(
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"prithivMLmods/Qwen2.5-VL-3B-Instruct-Unredacted-MAX"
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)
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
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},
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{"type": "text", "text": "Provide a detailed caption for this image."},
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],
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}
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]
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text = processor.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = processor(
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text=[text],
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images=image_inputs,
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videos=video_inputs,
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padding=True,
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return_tensors="pt",
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).to("cuda")
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generated_ids = model.generate(**inputs, max_new_tokens=256)
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output_text = processor.batch_decode(
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[out[len(inp):] for inp, out in zip(inputs.input_ids, generated_ids)],
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skip_special_tokens=True,
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clean_up_tokenization_spaces=False
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)
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print(output_text)
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```
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---
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## Intended Use
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* Multimodal AI research and evaluation
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* Image captioning and dataset generation pipelines
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* Vision-language prototyping and experimentation
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* Lightweight deployment in constrained environments
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* Development of multimodal applications and tools
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---
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## Limitations & Risks
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> **Important Note**: This model inherits behavior and constraints from its base architecture.
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* Performance depends on image quality, resolution, and prompt design
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* May produce incomplete or inaccurate interpretations in complex scenes
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* Requires adequate GPU resources for stable inference
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* Output consistency varies with decoding settings and runtime optimization |