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prithivMLmods
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Model Details
Model Provider
prithivMLmods
Model Tree
Qwen/Qwen3.5-35B-A3B
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-Qwen3.5-35B-A3B-abliterated
Optimized Repository Packaging Improved structure for cleaner downloads, versioning, and deployment workflows.
Updated Sharding Layout Enhanced weight distribution format for better loading performance and compatibility.
35B MoE Architecture (A3B) Built on Qwen3.5-35B-A3B, leveraging Mixture-of-Experts for scalable reasoning capacity.
Stable Transformers Integration Designed for smoother compatibility with modern Transformers versions and inference pipelines.
Preserved Model Behavior No changes to weights or architecture; performance remains consistent with the upstream base model.
Improved Deployment Reliability Reduced friction in initialization, multi-GPU usage, and distributed inference setups.
bash
pip install transformers==5.3.0# orpip install git+https://github.com/huggingface/transformers.gitpython
from transformers import Qwen3_5MoeForConditionalGeneration, AutoProcessorimport torch model = Qwen3_5MoeForConditionalGeneration.from_pretrained( "prithivMLmods/Qwen3.5-35B-A3B-Unredacted-MAX", torch_dtype="auto", device_map="auto") processor = AutoProcessor.from_pretrained( "prithivMLmods/Qwen3.5-35B-A3B-Unredacted-MAX") 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 MoE behavior and inference performance.
Red-Teaming & Evaluation Testing robustness under adversarial or complex prompting scenarios.
High-Performance Deployment Running large models on optimized multi-GPU or cloud inference systems.
Research Prototyping Experimentation with transformer scaling, architecture behavior, and deployment strategies.
Important Note: This model inherits behavior from its base model with minimal modification.
Output Variability Results may vary depending on decoding settings and prompt structure.
Resource Requirements A 35B MoE model requires substantial GPU memory or optimized inference techniques such as quantization or tensor parallelism.
Deployment Complexity Performance depends heavily on hardware configuration and runtime optimization.
General Model Limitations May still produce incorrect, incomplete, or inconsistent outputs in complex scenarios.
pip install transformers==5.3.0# orpip install git+https://github.com/huggingface/transformers.gitfrom transformers import Qwen3_5MoeForConditionalGeneration, AutoProcessorimport torch model = Qwen3_5MoeForConditionalGeneration.from_pretrained( "prithivMLmods/Qwen3.5-35B-A3B-Unredacted-MAX", torch_dtype="auto", device_map="auto") processor = AutoProcessor.from_pretrained( "prithivMLmods/Qwen3.5-35B-A3B-Unredacted-MAX") 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)