Qwen3.5-35B Heretic Model Preserves Multi-Token Prediction for Lightning Fast Local Generation
A fine-tuned Qwen 3.5 model arrives with native Multi-Token Prediction heads preserved, ensuring fast local inference. Use NVFP4 or GGUF formats to run it on consumer GPUs for uncensored coding tasks.
Why it matters
You can now run a highly competent 35B coding model on a consumer GPU at nearly twice the standard speed using hardware-optimized quantization formats.
TL;DR
- 01Download Qwen3.5 35B Heretic in NVFP4 or GGUF formats for optimized local performance
- 02Configure llama.cpp with draft models to enable the native Multi-Token Prediction speedup
- 03Use this model for secure security scripting and automated web scraping without refusals
Running local models for agentic coding usually means a trade-off between speed and intelligence. The Qwen 3.5 architecture is brilliant for coding, but default models are heavily censored, and speculative decoding often drops performance when modified. This release preserves the native Multi-Token Prediction (MTP) heads, allowing for extremely fast local generation when run under runtimes that support speculative decoding.
Unlike standard autoregressive models that predict one token at a time, Multi-Token Prediction models predict multiple future tokens in parallel during a single forward pass. By preserving all 785 native MTP structures, you can run this model in GGUF or Safetensors formats while utilizing speculative decoding pipelines to nearly double your local tokens-per-second output without losing the base model's reasoning capabilities.
If you are building a private, local agentic loop that auto-writes code, generates test suites, and refactors components, you can run this model on a single 24GB consumer GPU using NVFP4 or GPTQ-Int4 formats. The uncensored nature means it will never refuse to write scrapers or security-oriented test scripts.
To fully leverage MTP, you need runtime engines like llama.cpp or vLLM configured specifically to use the companion draft model, as standard vanilla inference runs treat it as a standard model, losing the speedup.
This model is a must-have local runner if you need uncensored coding intelligence with maximum execution speed.