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Nestleshuffle
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Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
Nestleshuffle
Model Tree
Base
llmfan46/Qwen3.5-35B-A3B-uncensored-heretic-v2-Native-MTP-Preserved
Quantized
this model
Input Modalities
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Output Modalities
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Supported Functionality
Dedicated Endpoints
bash
pip install mlx-lm
python
from mlx_lm import load, generatemodel, tokenizer = load("Nestleshuffle/Qwen3.5-35B-A3B-uncensored-heretic-v2-Native-MTP-Preserved-mlx-8Bit")prompt="hello"if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:messages = [{"role": "user", "content": prompt}]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)response = generate(model, tokenizer, prompt=prompt, verbose=True)