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prithivMLmods
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Model Details
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prithivMLmods
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prithivMLmods/Qwen3.6-35B-A3B-Uncensored-Aggressive
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.
Streamlined Inference Packaging Repository structure optimized for easier integration into large-scale inference workflows.
35B MoE Architecture (A3B) Built on Qwen/Qwen3.6-35B-A3B, leveraging Mixture-of-Experts design for scalable reasoning capacity.
Improved Deployment Stability Designed for more consistent loading and inference behavior across environments.
Preserved Model Behavior No modifications to weights or architecture; all behavior remains aligned with the original 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-Qwen3.6-35B-A3B-abliterated
bash
pip install transformers==5.8.0# orpip install git+https://github.com/huggingface/transformers.gitpython
from transformers import Qwen3_5MoeForConditionalGeneration, AutoProcessorimport torch model = Qwen3_5MoeForConditionalGeneration.from_pretrained( "prithivMLmods/Q3.6-35B-A3B-abliterated-0520-MAX-STOR-check", torch_dtype="auto", device_map="auto") processor = AutoProcessor.from_pretrained( "prithivMLmods/Q3.6-35B-A3B-abliterated-0520-MAX-STOR-check") 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 & Language Research Studying large-scale MoE behavior and inference characteristics.
Red-Teaming & Evaluation Testing robustness across complex and adversarial prompts.
High-Performance Local Deployment Running large Mixture-of-Experts models on multi-GPU 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.
High Compute Requirements A 35B MoE model requires significant GPU memory and 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 or incomplete outputs in complex scenarios.
pip install transformers==5.8.0# orpip install git+https://github.com/huggingface/transformers.gitfrom transformers import Qwen3_5MoeForConditionalGeneration, AutoProcessorimport torch model = Qwen3_5MoeForConditionalGeneration.from_pretrained( "prithivMLmods/Q3.6-35B-A3B-abliterated-0520-MAX-STOR-check", torch_dtype="auto", device_map="auto") processor = AutoProcessor.from_pretrained( "prithivMLmods/Q3.6-35B-A3B-abliterated-0520-MAX-STOR-check") 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)