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
Open Foundation Models for Learning and Teaching OmniEdu-4B-FP8 is the FP8-quantized release of OmniEdu-4B. The architecture, tokenizer, chat template and instruction-tuning data are unchanged: this checkpoint is the same model with its linear layers stored in FP8, which shrinks the weights from 8.46 GiB to 5.13 GiB and is intended for deployment on smaller GPUs and for higher-throughput serving. This checkpoint accompanies OmniEdu: Open Foundation Models for Learning and University · University of the Chinese Academy of Sciences · Zhongguancun Academy Quantized with LLM Compressor into the compressed-tensors FP8BLOCK scheme (library version 0.19.0), without calibration data: The vision…
Open weights
other
4.5B parameters
262,144 tokens
transformers
