Med-Qwen-3.5-4B-it

Model · Text generation

dQwen3.5-4B-Base

IFML

A masked diffusion language model adapted from Qwen3.5-4B. The backbone is hybrid: only its attention layers are made bidirectional, and the Gated DeltaNet layers stay causal. This is a base model, with no instruction tuning. Paper: dQwen3.5: Hybrid-Attention Diffusion Language Models. Code: https://github.com/AntonXue/dQwen Needs a CUDA GPU and transformers>=5.13 (tested with torch 2.7.1+cu128, flash-linear-attention 0.5.1). generate decodes the whole canvas at once, committing positions above a confidence threshold (tau=0.9); pass blocklength=32 for left-to-right block decoding, or tau=None, stepsperblock=k for a fixed budget. The 50B-token checkpoint from the paper is…

Model · Text generation

Huihui-NeoHorse-1-4B-abliterated

Huihui.ai

This is an uncensored version of TokenRhythm/NeoHorse-1-4B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. Layers 5-17 are being ablated (0-based indexing). The MTP and Visual components were extracted from the original Qwen/Qwen3.5-4B and can provide excellent support. If needed, you only need to copy the contents of MTP-Visual to overwrite the model directory. You can use this model in your applications by loading it with Hugging Face's transformers library: - Risk of Sensitive or Controversial Outputs: This model’s safety filtering…

Model · Text generation

JevK5-FP8

Liodon AI

FP8 quantization of alibiserikbay/JevK5, published by Liodon AI. Quantized with llm-compressor using the FP8DYNAMIC scheme: weights are cast to FP8 (E4M3) per-channel ahead of time, activations are quantized to FP8 dynamically per-token at inference time. No calibration dataset is needed for this scheme, so the quantized weights are numerically just a direct cast of the original — no calibration-set bias to worry about. lmhead is left unquantized (standard practice — negligible size, disproportionate quality impact if quantized). vLLM Text Generation Inference (TGI) SGLang FP8 execution requires an NVIDIA GPU with compute capability ≥ 8.9 (Ada/Hopper/Blackwell — RTX 40-series, L4/L40S…

Ask anything, get a how-to article. WikiQwen-4B is Qwen3.5-4B fine-tuned to answer every message the same way: as a tidy, step-by-step how-to article in markdown, with a picture caption above each step. Hand those captions to WikiQwen-Illustrator and they become illustrations. Send it a question ("how do I keep basil alive?"), a problem ("my bike chain keeps falling off") or just "hi", and it sends back a guide. Every reply has the same shape: - a # How to … title and a short intro - one or more ## Method N: or ## Part N: sections, or a single ## Steps - an [IMAGE: caption] line above each step, written for the Illustrator to draw - steps written as N. Bold summary. Details…, with bullets…

Model · Text generation

InterimLabs-Qwen3.5-4B-MLX-4bit

Interim Labs

This is a text-only, 4-bit affine/group-64 MLX derivative for Minstrel's optional Advanced local language engine. It is based on the pinned mlx-community/Qwen3.5-4B-4bit snapshot 0e7ffd5c629ef7719d4cbc04069232580bfa9d9c. The deriveqwen35text.py tool removed vision tensors and capped the packaged context at 8,192 tokens. The signed Minstrel manifest and provenance file are included. The manifest is for Minstrel's detailed-local slot, artifact version 0.1.0; it is not a general quality certification. The reference upstream is Qwen/Qwen3.5-4B at 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a. The source conversion does not establish the exact Qwen revision it used. The Apache-2.0 license file is…

Model · Text generation

Llama-3.3-70B-Instruct-CQ-4bit

ThousandML

Llama 3.3 70B Instruct quantized to 4-bit with Coded Quantization (CQ). CQ is a calibration-free 4-bit quantization method designed to preserve the behavior of the original model without requiring a calibration dataset. Because CQ does not optimize the quantization around a fixed calibration corpus, it is designed to generalize beyond calibration-specific data and workloads. This repository contains the 70B-parameter Llama 3.3 Instruct model quantized with Coded Quantization (CQ) 4-bit. Calibration-free. CQ does not require a representative calibration dataset before quantization. This avoids tying the quantization procedure to a particular calibration corpus and is intended to provide…