Model · Image and text to text
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, from v1.2, 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. On JevBench v1.6.1, H2O-Lightning-4B v1.1 is #1 on the official Composite Score of open-weight systems…
Open weights
apache-2.0
4.5B parameters
262,144 tokens
transformers
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…
Open weights
apache-2.0
4.5B parameters
262,144 tokens
transformers
This model is a fine-tuned version of Qwen/Qwen3.5-4B-Base on the Omni-Edu-70K dataset. The following hyperparameters were used during training: - learningrate: 5e-06 - trainbatchsize: 1 - evalbatchsize: 8 - distributedtype: multi-GPU - numdevices: 8 - gradientaccumulationsteps: 8 - totaltrainbatchsize: 64 - totalevalbatchsize: 64 - lrschedulertype: cosine - lrschedulerwarmupsteps: 0.1 - numepochs: 3.0 - Transformers 5.2.0 - Pytorch 2.10.0 - Datasets 4.0.0 - Tokenizers 0.22.2
Open weights
other
4.5B parameters
262,144 tokens
transformers
Model · Image and text to text
XinLi
The Label baseline of the DN-MOPD paper at Qwen3.5-4B continued to 160 updates (paper Table 5): multi-teacher on-policy distillation with label routing (each prompt is scored by the expert of its domain, every domain multiplier is 1). Released for comparison with DN-MOPD-Qwen3.5-4B; it is not the proposed method. Paper: Beyond Teacher Assignment: Domain-Normalized Multi-Teacher On-Policy Distillation (arXiv:2609.35347, project page) · Code: github.com/LiXin97/DN-MOPD The full recipe, with the launch scripts for every row of the paper's tables, is in recipes/qwen3.5/ and docs/recipe.md. This model was trained and evaluated with the non-thinking chat format. Pass enablethinking=False to the…
Open weights
apache-2.0
4.5B parameters
262,144 tokens
transformers
Model · Image and text to text
XinLi
A Qwen3.5-4B student trained with DN-MOPD (Domain-Normalized Multi-Teacher On-Policy Distillation) continued to 160 updates (paper Table 5). Three same-size RL experts (math, code, instruction following) teach one student on its own responses; each prompt is scored by the expert of its domain, and DN-MOPD rescales each domain's token-level feedback by its measured spread, wd = clip(σall / σd, 0.25, 4), so that no domain dominates the shared update. Paper: Beyond Teacher Assignment: Domain-Normalized Multi-Teacher On-Policy Distillation (arXiv:2609.35347, project page) · Code: github.com/LiXin97/DN-MOPD The full recipe, with the launch scripts for every row of the paper's tables, is in…
Open weights
apache-2.0
4.5B parameters
262,144 tokens
transformers
Model · Image and text to text
Everett
A typed-decision model (Jev-style /v1/systemone: choice, noul and score questions answered with a probability per option from one forward pass). LoRA fine-tune of Qwen/Qwen3.5-4B, merged into full weights, in two stages: 1. Stage 1 (ezjev-4b): LoRA r=16 on all language-model linear layers (attention, Gated-DeltaNet, MLP), LR 1e-4, one epoch over ~85k rows / ~106k questions. Loss: cross-entropy + Brier over the option labels, llm2jev chat prompt (thinking off). 2. Stage 2: a second LoRA at LR 5e-5 on ~30k rows targeting weak task families (RAG hallucination, product relevance, code-output selection, select-all-that-apply, clinical NLI, phishing emails, claim verification) with 40% replay.…
Open weights
other
4.5B parameters
262,144 tokens
transformers
