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prithivMLmods
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Model Details
Model Provider
prithivMLmods
Model Tree
Qwen/Qwen3.5-0.8B
Input Modalities
Output Modalities
Supported Functionality
This model has been re-sharded and optimized for the latest Transformers version from the base model: https://huggingface.co/huihui-ai/Huihui-Qwen3.5-0.8B-abliterated.
bash
hf auth login --token <YOUR_HF_TOKEN> hf download prithivMLmods/Gliese-Qwen3.5-0.8B-Abliterated-CaptionAdvanced Refusal Direction Analysis Uses targeted activation analysis to identify and mitigate refusal directions within the model’s latent space.
Abliterated Caption Training Fine-tuned for unfiltered and detailed caption generation, enabling comprehensive visual descriptions without excessive refusal behaviors.
Optimized Visual Understanding Enhanced to provide rich, context-aware descriptions of scenes, objects, people, and environments.
0.8B Parameter Architecture Built on Qwen3.5-0.8B, delivering efficient multimodal reasoning and caption generation while remaining lightweight and fast to deploy.
High-Fidelity Caption Generation Designed to produce structured and semantically detailed captions suitable for dataset generation, annotation, and research.
Efficient Deployment Suitable for caption dataset creation, multimodal research, local inference pipelines, edge deployments, and AI development workflows.
bash
pip install transformers==5.3.0# orpip install git+https://github.com/huggingface/transformers.gitpython
from transformers import Qwen3_5ForConditionalGeneration, AutoProcessorimport torch model = Qwen3_5ForConditionalGeneration.from_pretrained( "prithivMLmods/Gliese-Qwen3.5-0.8B-Abliterated-Caption", torch_dtype="auto", device_map="auto") processor = AutoProcessor.from_pretrained( "prithivMLmods/Gliese-Qwen3.5-0.8B-Abliterated-Caption") messages = [ { "role": "user", "content": [ {"type": "text", "text": "Describe this image in extreme detail."} ], }] 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=512) 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)Important Note: This model intentionally reduces built-in refusal mechanisms.
hf auth login --token <YOUR_HF_TOKEN> hf download prithivMLmods/Gliese-Qwen3.5-0.8B-Abliterated-Captionpip install transformers==5.3.0# orpip install git+https://github.com/huggingface/transformers.gitfrom transformers import Qwen3_5ForConditionalGeneration, AutoProcessorimport torch model = Qwen3_5ForConditionalGeneration.from_pretrained( "prithivMLmods/Gliese-Qwen3.5-0.8B-Abliterated-Caption", torch_dtype="auto", device_map="auto") processor = AutoProcessor.from_pretrained( "prithivMLmods/Gliese-Qwen3.5-0.8B-Abliterated-Caption") messages = [ { "role": "user", "content": [ {"type": "text", "text": "Describe this image in extreme detail."} ], }] 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=512) 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)