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prithivMLmods
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Model Details
Model Provider
prithivMLmods
Model Tree
Qwen/Qwen3.6-27B
Input Modalities
Output Modalities
Supported Functionality
| Metric | Result |
|---|---|
| Refusal Rate | N/A |
| Test Setup | N/A |
| Inference Type | text-generation |
| Dataset | N/A |
Note: This release does not introduce new benchmark evaluations and primarily focuses on repackaging, sharding updates, and Transformers compatibility improvements over the base model.
Latest Transformers Compatibility Re-sharded and optimized for improved compatibility with recent Transformers releases.
Optimized Model Sharding Updated shard structure for improved download reliability, storage handling, and inference efficiency.
Stable Inference Pipeline Improved packaging and structure for more consistent loading and generation behavior.
27B Architecture Built on Qwen/Qwen3.6-27B, providing strong reasoning and general language capabilities.
Improved Deployment Stability Designed for smoother inference across different hardware and runtime environments.
Preserved Model Behavior No changes to weights or architecture; behavior remains consistent with the base model lineage.
This model has been re-sharded and optimized for the latest Transformers version from the base model: https://huggingface.co/huihui-ai/Huihui-Qwen3.6-27B-abliterated
bash
pip install transformers==5.2.0# orpip install git+https://github.com/huggingface/transformers.gitpython
from transformers import Qwen3_5ForConditionalGeneration, AutoProcessorimport torch model = Qwen3_5ForConditionalGeneration.from_pretrained( "prithivMLmods/Qwen3.6-27B-Uncensored-Aggressive", torch_dtype="auto", device_map="auto") processor = AutoProcessor.from_pretrained( "prithivMLmods/Qwen3.6-27B-Uncensored-Aggressive") 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)Multimodal and Language Research Studying large-scale transformer behavior and inference characteristics.
Red-Teaming & Evaluation Testing robustness across challenging and adversarial prompts.
High-Performance Deployment Running large language models on optimized hardware setups.
Research Prototyping Experimentation with scalable transformer architectures.
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 27B parameter 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.
pip install transformers==5.2.0# orpip install git+https://github.com/huggingface/transformers.gitfrom transformers import Qwen3_5ForConditionalGeneration, AutoProcessorimport torch model = Qwen3_5ForConditionalGeneration.from_pretrained( "prithivMLmods/Qwen3.6-27B-Uncensored-Aggressive", torch_dtype="auto", device_map="auto") processor = AutoProcessor.from_pretrained( "prithivMLmods/Qwen3.6-27B-Uncensored-Aggressive") 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)