Qwen3.5-4B

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Lodestar-4B

StartLux

Lodestar-4B is a 4-billion-parameter decision model. You give it a state (plain text, JSON or a long document) and one or more typed questions; it returns a probability for every listed option. Each answer is read from a single forward pass. The model never generates free text, so there is nothing to parse and no output length to budget for. (Probabilities rounded to three decimals.) The same call from Python, run inside the downloaded folder: A score question takes its levels as a list, lowest first, and returns {"type": "score", "score":, "probabilities": {"0": p0, "1": p1,...}}. Every question is rendered into one chat prompt (thinking disabled): The probability of each option is the…

Valen-4B combines Qwen3.5-4B with a two-layer MLP-Mixer decision head. It predicts probability distributions for Choice, Noul (yes/no), and Score questions. A shared state can contain text, images, or video and serve multiple questions. Valen-4B 基于 Qwen3.5-4B 和两层 MLP-Mixer 决策头，支持图文、文本、视频，以及共享 state 的多题推理。 These weights include merged LoRA adapters. BF16 rounding can cause small probability differences from the original training checkpoints used for the reported benchmarks. The verified environment uses Python 3.10, PyTorch 2.6.0, Transformers 5.4.0, torchvision 0.21.0, Pillow, and av 16.1.0. Flash Attention 2 requires a compatible flash-attn installation. For a local copy, replace the…

LT-OPD trains vision-language models with fewer visual tokens through on-policy self-distillation. This Qwen3.5-4B model combines CDPruner with an MLP that aggregates discarded tokens into their nearest retained tokens, preserving their original positions without adding tokens. Use Python 3.12 and the CUDA environment in the installation guide. Load the model with LT-OPD's visual-token compression runtime

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h2o-lightning-4b

H2O.ai

H2O-Lightning-4B is a 4B-parameter decision model from H2O.ai, built on Qwen/Qwen3.5-4B. It answers typed decision questions about a record (a document, a ticket, a policy, a conversation) and about images that come with the record (photos, screenshots, scanned documents, charts). It returns a probability for every option: - yes/no (noul): gives the probability that a statement is true; It runs on unmodified vLLM 0.30.0 with a small standard-library shim in front (h2olightningshim.py). Each decision is one forward pass and one output token, so the cost is input tokens only. Try it in your browser: live demo on Hugging Face Spaces (text and images; built by the Hugging Face team and running…

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Vinci-Piccolo-1.0

SimpleDirect

Vinci Piccolo is a small, open-weight chat model fine-tuned for character and honesty — the first model in the Vinci family from SimpleDirect. The character you'd want in an AI, open and small enough to run yourself. Try it: chat app — free · ollama run hf.co/simpledirect/Vinci-Piccolo-1.0-GGUF Weight-file size is not a runtime-memory requirement — model loading, KV cache, context length and batching all need memory beyond the weights. See Hardware requirements below for the figures we do give. Most fine-tuning optimizes for capability. Vinci Piccolo is fine-tuned for something else: a consistent character and an honest disposition. It is trained against a written, public Constitution that…

INT4 weight-only GPTQ quantization of Qwen/Qwen3.5-4B for memory-constrained single-GPU serving. Also the verifier for the (DFlash2 speculative decoding, τ 3.8 at the serving point / 4.1 greedy vs native MTP 2.9 / 3.0 on the same ladder). - W4A16 (INT4 weights, BF16 activations), group 128, act-order, compressed-tensors (pack-quantized) — vLLM Marlin kernels - Calibrated with llm-compressor dataset-native proportions from (~2.6M effective tokens). The exact rows are hash-pinned in — that pin is the citable calibration generation. (A -cascade suffix on local paths in the lab repo refers to the same generation; this repo carries only the current one and supersedes the initial pre-cascade…