サイト内検索
AI モデル 2119
- Qwen3.8-27B-DFlash2↗
incoai
This repository contains the DFlash 2 draft model for `Qwen/Qwen3.8-27B`. It is not a standalone language model: it runs inside a speculative decoding server and drafts tokens for the target model to verify. The checkpoint is also mirrored at `z-lab/Qwen3.8-27B-DFlash2`. DFlash 2 is a block-diffusion drafter for speculative decoding. It predicts a whole block of tokens in a single pass and keeps the top candidates at every position. A lightweight selector then traces one coherent path through them. Two-tap dynamic convolutions in the backbone keep the draft from decaying toward the end of the block. Decoding is lossless: greedy output matches the target model exactly, and sampling preserves its distribution. See the blog post for other engines and more details.
- indo-sentiment-analysis↗
agufsamudra
This model is fine-tuned from **`indobenchmark/indobert-base-p1`** for binary sentiment classification (Positive/Negative) on Indonesian text. - **Developed by:** agufsamudra - **Model type:** Text Classification - **Language(s):** Indonesian (id) - **License:** Apache-2.0 - **Fine-tuned from model:** indobenchmark/indobert-base-p1 This model is intended for binary sentiment classification tasks in Indonesian language texts. It predicts whether a given text expresses positive or negative sentiment.
- Qwen3.8-27B-DFlash2-GGUF↗
incoai
This repository contains GGUF conversions of `incoai/Qwen3.8-27B-DFlash2`, the DFlash 2 draft model for `Qwen/Qwen3.8-27B`. It is not a standalone language model: it runs inside a speculative decoding server and drafts tokens for the target model to verify. The checkpoints are also mirrored at `z-lab/Qwen3.8-27B-DFlash2-GGUF`. DFlash 2 is a block-diffusion drafter for speculative decoding. It predicts a whole block of tokens in a single pass and keeps the top candidates at every position. A lightweight selector then traces one coherent path through them. Two-tap dynamic convolutions in the backbone keep the draft from decaying toward the end of the block. Decoding is lossless: greedy output matches the target model exactly, and sampling preserves its distribution. Build llama.cpp with DFlash 2 support (PR #27342):
- GLM-5.3-Flash-Strata-Q2Q3-Experts↗
lte-ai
Experimental GGUF weights for running **GLM-5.3-Flash** on a normal PC with the LightTransport Strata fork: expert sidecars and a complete full model for a 128 GB host with a 16 GB NVIDIA GPU, a smaller REAP-224 model, and a complete REAP-50 model for a 64 GB host with a 16 GB AMD GPU. The sidecars requantize routed expert gate/up weights from IQ2_XS to Q2_K and down weights from IQ3_XXS to Q3_K; this changes weight values and is neither a lossless repack nor a fine-tune. REAP-224 keeps 224 of 288 routed experts per layer; REAP-50 keeps 144. Read the quality numbers below before choosing a model. Current measurements, setup for one and two GPUs, and context-length limits are in `docs/GLM53F_RELEASE.md` on the fork's `glm53f` branch; bringing it up on other hardware is described in `docs/GLM53F_NEW_HARDWARE.md`. The conversion recipe is `docs/GLM_RELEASE_Q2Q3.md`. **On a 128 GB machine use `GLM-5.3-Flash-Q23-FP8-assembled.gguf`** (or `experts-q23-fp8.gguf` with the Unsloth shards); **on a 64 GB machine use `reap50/GLM-5.3-Flash-REAP50-Q23-FP8-assembled.gguf`.** Their routed experts have the same types as Q23 (Q2_K gate/up, Q3_K down) but are quantized once, directly from the official FP8 weights, instead of from Unsloth's 2-bit experts (REAP-50: from its Q4_K_M experts). Measured against exact logits of the FP8 release on 16 held-out sequences (8,192 tokens):
- Gemma-4-E4B-Samantha-i1-GGUF↗
mradermacher
- DaSiWa-MiniMax-H3-Hybrid↗
brurpo
Unmodified 4-step and 8-step DaSiWa MiniMax H3 Hybrid SafeTensors checkpoints mirrored for BRP Canvas downloads. The hybrid checkpoint supports text-to-video, reference-to-video, and first/last-frame-to-video through the same ComfyUI workflow. Original model: DaSiWa MiniMax H3 Hybrid on Civitai This is not an official MiniMax or DaSiWa distribution. Review the original model page and the included upstream MiniMax license before use.
- hyenadna-tiny-1k-seqlen-hf↗
LongSafari
Welcome! HyenaDNA is a long-range genomic foundation model pretrained on context lengths of up to **1 million tokens** at **single nucleotide resolution**. You'll see models of different sizes and sequence lengths. There are also original weights-only versions of each model in the LongSafari organization, which are designed to be loaded with the original github repo. These models have identical outputs to the models in the collection above, just different interfaces. In this brief code sample we demonstrate fine-tuning HyenaDNA on a sequence classification task. This sample uses the `medium` checkpoint, with a maximum sequence length of 160k nucleotides. Note that training will fail if you use a sequence length longer than the maximum supported length for your chosen checkpoint.
- opensearch-neural-sparse-encoding-doc-v2-distill↗
opensearch-project
The model should be selected considering search relevance, model inference and retrieval efficiency(FLOPS). We benchmark models' **zero-shot performance** on a subset of BEIR benchmark: TrecCovid,NFCorpus,NQ,HotpotQA,FiQA,ArguAna,Touche,DBPedia,SCIDOCS,FEVER,Climate FEVER,SciFact,Quora. Overall, the v2 series of models have better search relevance, efficiency and inference speed than the v1 series. The specific advantages and disadvantages may vary across different datasets. - **Paper**: Towards Competitive Search Relevance For Inference-Free Learned Sparse Retrievers - **Fine-tuning sample**: opensearch-sparse-model-tuning-sample
- NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-GGUF↗
AtomicChat
Atomic Dynamic (AD) quants, built from NVIDIA's original BF16 weights with our own importance matrix. Every file here matches or beats the other GGUF builds of the same size or larger, measured side by side on one stand. The calibration corpora behind our builds are public. Every file here was measured against the BF16 original on the same stand as the other publishers' files, and each one beats or ties the file of theirs that is the same size or larger. You can now run Nemotron 3.5 Super in Atomic Chat : 120B total parameters, 12B active per token. See our quantization analysis below for measurements and instructions. Lower is closer to the original. Each purple point is a file in this repo; the grey ones are the other publishers' files, downloaded and measured with exactly the same reference, text and llama.cpp build. At 75 GB, `AD-IQ4_NL` drifts about half as much as both NVFP4 builds, which are the same size or up to 3 GB larger. At 70 GB, `AD-IQ3_M` drifts about half as much as LM Studio's `Q4_K_M` of the same size. At 65 GB, `AD-IQ3_XXS` drifts 20% less than Unsloth's `UD-Q3_K_XL`, and from 46 to 58 GB the `AD-Q2_0_*` files drift 6 to 18% less than the Unsloth file of each size, and at 87 GB `AD-Q4_K_S` drifts 13% less than `UD-Q4_K_XL`.
- Qwen-Image-2.1-Turbo-GGUF↗
DogukanUrker
GGUF quants of Qwen/Qwen-Image-2.1-Turbo, the 8-step distilled Qwen-Image-2.1 (7B DiT). Converted with stable-diffusion.cpp from the official diffusers weights on release day. Every quant is tested on a single RTX 3060 12GB. These are made for **stable-diffusion.cpp** (`sd-cli`, `sd-server`, and anything built on it, like llama-swap). They are not ComfyUI-GGUF files. Using ComfyUI? See the section at the bottom. `Q4_K_S` and `Q3_K_S` are uniform k-quants (every quantized tensor is Q4_K or Q3_K). `qwen-image-2.1-turbo-Q4_K.gguf` and `qwen-image-2.1-turbo-Q3_K.gguf` are the exact same files under their old names, kept so existing links keep working.