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AI 모델 2119
- LTX-2.5-Quantized↗
joeygambino
LTX-2.5 generates picture and sound in one pass. It is also 42 GB of bf16 weights, and the text encoder beside it is another 26 GB. This repo is the set of weights that make it run on a card that does not have that. Sizes are measured, not estimated. "Fits 16 GB" assumes the text encoder is not resident at the same time — ComfyUI frees it before sampling — and tiled VAE decode. File sizes are decimal GB (bytes / 10^9); card capacities are the usual binary GiB, so a 15.9 GB file occupies 14.8 GiB of a 16 GiB card. The gap is real and it is where your activation pool lives. On the text encoder: at bf16 it is 26.3 GB and at Lightricks' int8 15.4 GB, which on a 16 GB card means the encoder alone owns the machine for the one step that just makes an embedding. **No GGUF of the LTX-2.5 encoder exists** — ComfyUI-GGUF gates text encoders on `TXT_ARCH_LIST`, which carries `gemma3` but not `gemma4`, so LTX-2.3's gemma3 encoder does have GGUFs and this one cannot. The open request (#450, 2026-05-28) has not been picked up — but comfy-native quantisations of it do exist and landed the same day as ours (Winnougan's w4a8-convrot at 10.6 GB, DmitryDB's nvfp4 at 11.2, vonkaiser's nvfp4-torchao at 8.9). Ours is w4a8 at 10.60 GB, within 2 MB of Winnougan's — the same format at the same size, published independently. Treat it as a mirror, not a first.
- flan-t5-base-samsum↗
philschmid
- Apertus-v1.1-0.5B↗
swiss-ai
Apertus-v1.1 is a series of highly efficient, 0.5-4B billion parameter language models designed to extend the fully-open and compliant Apertus ecosystem to highly constrained hardware environments. The models rely on a dense transformer architecture featuring grouped-query attention and xIELU activations. To achieve high performance with a minimized memory footprint, this model uses tied embeddings and a deeper, thinner architectural design. Instead of standard pre-training, Apertus-v1.1 models were created using pre-training distillation (PD) from the Apertus-8B-2509 teacher model. They were trained on 1.7T tokens from Phase 5 of the original Apertus data pipeline—the highest quality tier of filtered documents, code, and instruction samples without introducing any new data sources or licenses.
- gemma-4-E4B-it-GGUF↗
TheStageAI
At 3.76 GB, S matches the BF16 instruction-strict IFEval score within evaluation variation. It saves 577 MB versus M while staying within 0.18 percentage points on strict IFEval. P / I means prompt-strict / instruction-strict. IFEval uses deterministic non-thinking decoding; MMLU-Pro uses sampled long-form reasoning. Only complete model-level scores are reported. A dash means not reported. All four checkpoints use the same production PTQ pipeline. The precision map is the only tier-specific part.
- Yuan-embedding-2.0-zh↗
IEITYuan
Yuan-embedding-2.0-zh 是专门为中文文本检索任务设计的嵌入模型。我们在Yuan-embedding-1.0的基础上，针对Retrieval任务与Reranking任务进行了进一步优化。主要工作如下： - 数据增强 - Hard negative sampling：利用Rerank模型与LLM进行双重评估，筛选出高质量正负样本 - LLM合成数据：利用Yuan2-M32针对训练数据query进行LLM重写 - 损失函数设计 - Multi-Task loss - Matryoshka Representation Learning - Retrieval任务使用InfoNCE with in-batch-negative - 针对Reranking任务设计Margin-Adaptive Pairwise Ranking Loss
- BioLinkBERT-large↗
michiyasunaga
BioLinkBERT-large model pretrained on PubMed abstracts along with citation link information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository. This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as BLURB and MedQA-USMLE. LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures **document links** such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document.
- sambanovasystems_-_SambaLingo-Arabic-Base-gguf↗
RichardErkhov
- TwIL-LM3-Pro↗
webAI-Official
A 3.66B reasoning model for **formal logic** tasks, built from `ibm-granite/granite-4.2-3b` through LoRA supervised fine-tuning, checkpoint fusion, WiSE-FT weight interpolation, and entropy-weighted GRPO reinforcement learning. It improves in-domain formal-logic performance by **+28% relative** over its base model (macro gate 0.431 → 0.554) **while holding held-out benchmark performance** — the 10-dataset macro is level with the base (0.7942 → 0.7901) and the 14-dataset macro improves slightly (0.7332 → 0.7425). On the same harness and sampled rows it reaches the highest Track A macro gate, strict-7 and six-lane average of any arm in the tables below for which each can be computed, including Qwen3-8B, Qwen3.5-4B, VibeThinker-3B and gpt-oss-120b (the 120B has no gate or strict-7 value). *VibeThinker-webAI-trained is the public VibeThinker-3B after the same post-training pipeline (SLERP, d = 0.5, t = 0.5); see Against the tuned VibeThinker-3B. It has no strict-7 or six-lane-average value, so those bars show n/a.*
- BAGEL-7B-MoT↗
字节跳动
> We present **BAGEL**, an open‑source multimodal foundation model with 7B active parameters (14B total) trained on large‑scale interleaved multimodal data. BAGEL outperforms the current top‑tier open‑source VLMs like Qwen2.5-VL and InternVL-2.5 on standard multimodal understanding leaderboards, and delivers text‑to‑image quality that is competitive with strong specialist generators such as SD3. Moreover, BAGEL demonstrates superior qualitative results in classical image‑editing scenarios than the leading open-source models. More importantly, it extends to free-form visual manipulation, multiview synthesis, and world navigation, capabilities that constitute "world-modeling" tasks beyond the scope of previous image-editing models. This repository hosts the model weights for **BAGEL**. For installation, usage instructions, and further documentation, please visit our GitHub repository. BAGEL adopts a Mixture-of-Transformer-Experts (MoT) architecture to maximize the model’s capacity to learn from richly diverse multimodal information. Following the same principle of capacity maximization, it utilizes two separate encoders to capture pixel-level and semantic-level features of an image. The overall framework follows a Next Group of Token Prediction paradigm, where the model is trained to predict the next group of language or visual tokens as a compression target.
- index-echo-2b-GGUF↗
cstr
Conversion of IndexTeam/Index-Echo-S2TT-2B for the `index-echo` backend in CrispASR. Weights have passed the independent CPU/CUDA checks below. The backend is available on CrispASR main, including C ABI loading, companion downloads and a pinned nightly regression. The released model transcribes Chinese and translates each subtitle into English, Japanese or Spanish. CrispASR preserves the three-line generation recipe and returns bilingual cues with timestamps and punctuation. English JFK transcription/English translation also matches the released Python model in the CPU fixtures; this is a port-parity result, not a language-wide accuracy claim. The 9B checkpoint is not included. Both files of the chosen precision must be in the same directory. The tower's `index_echo.decoder_file` metadata names its decoder; point CrispASR at the tower, not the decoder. The decoder omits the absent MTP branch and contains exactly the 24 text blocks used by the released speech inference path. Vision weights are omitted.