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AI 모델 2119
- SaProt_650M_AF2↗
westlake-repl
Note: SaProt requires structural (SA token) input for optimal performance. AA-sequence-only mode works but must be finetuned - frozen embeddings work only for SA, not AA sequences! With structural input, SaProt surpasses ESM2 in most tasks. --> We provide two ways to use SaProt, including through huggingface class and through the same way as in esm github. Users can choose either one to use. The esm version is also stored in the same folder, named `SaProt_650M_AF2.pt`. We provide a function to load the model.
- parakeet-tdt-0.6b-v3-GGUF↗
cstr
GGUF / ggml conversions of `nvidia/parakeet-tdt-0.6b-v3` for use with the `parakeet-main` CLI from **CrispStrobe/CrispASR**. This repo provides four quantisations, all converted from the same `.nemo` checkpoint via the streaming `convert-parakeet-to-gguf.py` script and quantised with `cohere-quantize`. All quantisations produce identical text on `samples/jfk.wav`: > And so my fellow Americans. Ask not what your country can do for you. Ask what you can do for your country.
- medsam-vit-base↗
flaviagiammarino
- SenseVoiceSmall-gguf↗
handy-computer
GGUF conversions of FunAudioLLM/SenseVoiceSmall for use with transcribe.cpp. Ported from upstream commit 3eb3b4e, pinned 2026-05-06. Validated against the FunASR reference at transcribe.cpp commit f094d28 on 2026-05-06. Offline multilingual speech-to-text in Chinese, Cantonese, English, Japanese, and Korean. A SAN-M encoder with a single CTC head over a 25,055-token SentencePiece vocabulary. Takes a 16 kHz mono WAV (capped at 30 seconds per call, per upstream's direct-inference contract) and produces a transcript. Not a streaming model, no translation, no built-in long-form chunking. The same CTC head also emits language-ID, simple emotion labels, audio-event tags, and inverse-text-normalization control tags. These tags are hidden unless `--raw-tokens` is passed. ITN is on by default for readable casing, punctuation, and digits; pass `--no-itn` for upstream's spoken-form output.
- OpenThai-SystemOne↗
iapp
**An open Thai + English "System One" decision model.** It does not generate text. Given a *state* (any text or JSON) and typed *questions*, it returns calibrated probabilities over the options in **one forward pass**: The request/response contract mirrors TypeSafe's `POST /v1/systemone` so code written for the TypeSafe SDK can be pointed at this model unchanged. Typical uses: ticket routing, moderation, intent detection, RAG relevance judging, LLM-output verification, and **computer-use / browser-agent action selection** (which element to click, which tool to call). Ollama 0.35 serves System One models locally at `POST /v1/systemone`, with the same Jev-style contract. OpenThai-SystemOne has an **Ollama build**: `iapp/openthai-systemone` on ollama.com (tags `0.8b` = `0.8b-q8_0`, `0.8b-q4_K_M`, `0.8b-bf16`), also as GGUF on Hugging Face: `iapp/OpenThai-SystemOne-Ollama`. No API key; nothing leaves your machine.
- SGuard-ContentFilter-2B-v1↗
SamsungSDS-Research
- Llama-3.1-Swallow-8B-Instruct-v0.5↗
tokyotech-llm
Llama 3.1 Swallow is a series of large language models (8B, 70B) that were built by continual pre-training on the Meta Llama 3.1 models. Llama 3.1 Swallow enhanced the Japanese language capabilities of the original Llama 3.1 while retaining the English language capabilities. We use approximately 200 billion tokens that were sampled from a large Japanese web corpus (Swallow Corpus Version 2), Japanese and English Wikipedia articles, and mathematical and coding contents, etc (see the Training Datasets section of the base model) for continual pre-training. The instruction-tuned models (Instruct) were built by supervised fine-tuning (SFT) on the synthetic data specially built for Japanese. See the Swallow Model Index section to find other model variants. **Note**: Llama-3.1-Swallow-8B-Instruct-v0.5 model was continually pre-trained from the meta-llama/Llama-3.1-8B-Instruct and then instruction-tuned with our instruction datasets. This release enhances the conversation capability of Llama 3.1 Swallow. The model is trained to imitate the behavior of gemma-3-27b-it. Among all open-source LLMs with <= 8 billion parameters, Llama-3.1-Swallow-8B-Instruct-v0.5 exhibits **state-of-the-art performance on Japanese MT-Bench**, outperforming its predecessor, Llama-3.1-Swallow-8B-Instruct-v0.3, by 1.5 points.
- GLM-5.3-Flash-GGUF↗
vcruz305
Community llama.cpp GGUFs of Z.ai's GLM-5.3-Flash (`Glm5NextForConditionalGeneration`, 320B-A18B, native multimodal). **This pack is the first working GGUF conversion of GLM-5.3-Flash.** The convert registers GGUF arch `glm5next` and maps the 5.3 hybrid end-to-end: **KDA** (gated delta-net), **NoPE sparse MLA** + lightning indexer + **IndexPool**, **mHC**, **288-expert MoE** + shared expert, **MTP / NextN**, and the **vision tower as mmproj**. It is a 5.3 convert, not a GLM-5.2 `GlmMoeDsa` class rename (that path only covers the 11 MLA blocks and drops the 34 KDA layers). `llama-quantize` on that same `glm5next` build wrote the K-quants below (Q2_K 2.91 BPW, Q4_K_M 4.81 BPW). **Run** these GGUFs on vcruz305/llama.cpp `main` / `glm5next-mtp` (`ea37b8b`): Unsloth glm5next graph plus **NextN `graph_mtp`**, with the trunk emitting `t_h_nextn` so `--spec-type draft-mtp` can accept. ggml-org drafts #27752, #27754, #27773 load the weights; MTP decode is on this fork.
- index-echo-9b-GGUF↗
cstr
Native speech translation with bilingual, timestamped subtitles, primarily Chinese → English, Japanese or Spanish. This repository contains the validated F16 encoder/connector and decoder pair for CrispASR. Keep both GGUF files in the same directory. Use a CrispASR build containing the 9B integration; earlier 2B-only builds cannot load this pair. `--target-lang` selects `en`, `ja` or `es`. Complete-file processing also uses the standard `ggml-silero-v6.2.0.bin` VAD companion; `--auto-download` fetches it. The 2B model remains CrispASR's smaller default. This 9B repository supports F16 only.
- Qwen3.8-27B-int4-AutoRound↗
Frozenlock
INT4 (W4A16) AutoRound quantization of Qwen/Qwen3.8-27B — **18 GB**, with the **MTP speculative-decoding head quantized and working**. The MTP head is quantized along with the model, so `--speculative-config` in vLLM works out of the box and roughly **doubles decode throughput** (measured +98 % on temp-0 prose vs the same GPU without MTP; draft acceptance 41–67 % depending on content). At the time of upload, the available 4-bit options either dropped the MTP tensors (no speculative decoding) or kept hundreds of tensors in bf16 (28 GB — no longer fits a 24–32 GB GPU next to a useful KV cache). This one follows the recipe that served Qwen3.6-27B in production for months: