Open-Weight Models

Model

LightJev-0.6B-v0.1

Rongxinzy

A trained finite-candidate decision prototype built on Qwen3-0.6B by RongxinAI. Full weights, tokenizer, training logs, frozen gold-only data and held-out predictions are included. This is a synthetic-domain research release, not a general business decision model or an official Jev reproduction. Selected arm: CE, checkpoint step 250, chosen only by development cross-entropy. This selected checkpoint contains 20 head-only plus 230 full-model updates (4,000 sampled questions); each completed training arm ran the longer 20 + 300 update budget (5,120 sampled questions). It has 596,052,993 parameters, including a shared 3,073-parameter LayerNorm/scalar scoring head. Choice, Boolean and ordinal…

Model · Image and text to text

LightOnOCR-3-0.8B-ONNX-web

superPDF

lightonai/LightOnOCR-3-0.8B by LightOn, converted to ONNX for superPDF, where it powers high-accuracy reading in the browser (WebGPU, superpdf.ai) and the command-line tool (default model). It runs with Transformers.js on ONNX Runtime (WebGPU), in the browser and in Node. from the unused multi-token-prediction layer). Every weight in those graphs is recomputed from LightOn's weights and written at the same place, so the graphs themselves are unchanged. asymmetric with zero points, computed with ONNX Runtime's own quantizematmul4bits); vision encoder float16. Norm weights are stored as 1 + w and the gated delta net's A as -exp(Alog), as the builder expects. - These are the same bytes as…

Model · Image and text to text

LightOnOCR-3-4B-ONNX

superPDF

lightonai/LightOnOCR-3-4B by LightOn, converted to ONNX for superPDF, where it powers high-accuracy reading in the command-line tool and MCP server (--model 4b, model: "4b"). It runs with Transformers.js on ONNX Runtime (WebGPU), in the browser and in Node. from the unused multi-token-prediction layer). Every weight in those graphs is recomputed from LightOn's weights and written at the same place, so the graphs themselves are unchanged. asymmetric with zero points, computed with ONNX Runtime's own quantizematmul4bits); vision encoder float16. Norm weights are stored as 1 + w and the gated delta net's A as -exp(Alog), as the builder expects. - Converted by superPDF with the converter below.…

This quant collection REQUIRES ikllama.cpp fork to support the ik's latest SOTA quants and optimizations! Do not download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc! NOTE ikllama.cpp can also run your existing GGUFs from bartowski, unsloth, mradermacher, etc if you want to try it out before downloading my quants. Some of ik's new quants are supported with Nexesenex/croco.cpp fork of KoboldCPP with Windows builds for CUDA 12.9. Also check for Windows builds by Thireus here. which have been CUDA 12.8. These quants provide best in class perplexity for the given memory footprint. Shout out to Wendell and the Level1Techs crew, the…

Model

Ling-3.1-flash-VQ-GGUF

Sunghoon Baek

Experimental VQ8 conversion of the official text-only pinned to a4ccd6b45c002ddb236f9cb77d52cc427482bf19. This release uses a custom ling31vq GGUF family. It requires a runtime that implements FORMAT.md; ordinary llama.cpp, Ollama and Transformers loaders do not implement this format. The ds4-dfm-rs work includes artifact readers and numerical reference tools. A complete GB10 serving backend is a subsequent development step. and final held-out evaluation are in progress. This initial publication contains the model card, format specification and original model assets; GGUF weights will be uploaded after conversion and validation. The precision policy below is the selected release policy…

Model

Ling-mini-2.0-oQ8e

Scott Lowry

This model was quantized using oQ (oMLX v0.7.0.dev4) mixed-precision quantization.

Task6–9 each contain an independently fine-tuned LoRA checkpoint for the Franka GELLO demonstrations. Task4 is being prepared and will be added separately. Task5 is excluded. Each task starts independently from robbyant/lingbot-va-base at revision 68b7bc1b35da6ddc67ea94c4ceb58d768fbb3f9c. The original base is required. Do not use JAM's adapter loader or the default LingBot bimanual preset. See INFERENCE.md for downloads, installation, the GPU server, the extended GELLO client and the exact action contract. The history interface is required: it retains up to 129 real RGB observations at 15 Hz. An unmodified current-image-only JAM client does not provide the trained visual context. Task6–9…

static quants of https://huggingface.co/TCMLLM/Lingdan-4B-Base For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And…

static quants of https://huggingface.co/TCMLLM/Lingdan-4B-R1 For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And…

static quants of https://huggingface.co/TCMLLM/Lingdan-8B-R1 For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And…

Model · Image and text to text

Lite-OmniJev

Wang

冻结 Gemma 4 E4B 基座 + 决策 LoRA，每题一次前向读出全部选项概率。 支持 Choice（2–20项）、Score（2–10级）、yes/no，覆盖文本、图像、截图、音频与视频。 API直接返回答案概率、置信度和结构化判断。 MMAU音频路由基座方案采用65.9%的实测结果；默认部署脚本采用全模态adapter。 真实模型工作台通过三类判断、图像/截图/音频/视频、cURL重放及错误恢复验证。 GGUF三档在20个公开文本案例上均与参考选答20/20一致，截图、音频和视频运行探针全部成功。 环境：Linux x8664、Python3.11、兼容CUDA13的NVIDIA驱动；FP32配方在H20 96GB实测显存约39GB。 终端B启动API和工作台： 打开本地工作台，或调用API： 每题返回probabilities、confidence及相应的choice、score或noul。 Score为从0开始的期望等级；Noul为yes的概率。部署脚本使用原始答案概率（calibration: unfitted）。 媒体通过服务端受信文件路径和SHA256引用： type也支持audio/video；视频用videoframes选择8或16帧。 GGUF截图采用RGB、长边≤1344；完整命令见gateway/GGUF.md。

Model · Text to image

LittleBunnyGrl

AiMamis

You should use Lisa to trigger the image generation. You should use Curly black hair to trigger the image generation. You should use Long eyelashes to trigger the image generation. You should use Medium skin to trigger the image generation. Download them in the Files & versions tab.