bella-bartender-gemma-e4b-gguf
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Explore AI models 85001
bella-bartender-heretic-1b
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View details ↗bella-bartender-qwen3-1.7b
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View details ↗bella-bartender-qwen3.5-2b
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View details ↗lora
JujoHotaru- 十条蛍（Hotaru Jujo）の作成したLoRAを配布しています。 - You can download Hotaru Jujo's LoRA collection from this repo. - 作者プロフィール / Author's profile - すべてのLoRAはMITライセンスまたはCreativeML Open RAIL-Mのデュアルライセンスでリリースされます。どちらかのライセンスを選択して使用できます。 - All LoRA's are dual-licensed under MIT LICENSE or CreativeML Open RAIL-M. - LoRAの使用にあたって事前承諾や事後報告などは一切必要ありませんが、TwitterなどSNSで紹介していただけると嬉しいです。 - No prior consent or after reporting is required for the use of LoRA, but I would appreciate it if you could introduce it on Twitter or other SNS. - 配布中のLoRAは、特記していない限りCFG Scale 7、Clip skip 1を標準設定として開発・動作検証しています。 - Unless otherwise noted, all LoRA's are developed and tested on "CFG Scale 7" and "Clip skip 1" settings. "Eye collection" is a series of LoRAs designed to reproduce various eye shapes without depending on data models. "Eye collection" is a series of LoRAs designed to reproduce various eye shapes without depending on data models.
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗This repo contains pre-generated control vectors in GGUF format for use with llama.cpp: - **IMPORTANT**: These **new control vectors** must use their **respective de-bias control vector(s)**. - The code used to generate these can now be found at github.com/jukofyork/control-vectors. - All were generated with `'--num_prompt_samples'` set to the model's hidden state dimension. Control vectors allow fine-tuned control over LLMs, enabling more precise/targeted text generation.
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View details ↗roberta-ner-multilingual
julian-schelb基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗MiniCPM5-2B-NPU2
julianmb基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Ornith-1.5-35B-A3B-ROCmFP4-GGUF
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View details ↗Qwen-3.8-27B-ROCmFP4-FAST-GGUF
julianmbThis repository contains optimized **ROCmFP4** and **ROCmFP8** GGUF releases of **Qwen 3.8 27B**, custom-engineered for **AMD Strix Halo (Ryzen AI Max+ 395 / Radeon 8060S)** and **AMD Radeon discrete GPUs**. > 🛠️ **Unified HaloFPX Server:** github.com/julianmb/halofpx (Multi-model zoo, hot-swapping, OpenAI API) > 📖 **Qwen 3.8 Dedicated Deep-Dive:** github.com/julianmb/q38rocm (Standalone optimization report, benchmarks & tuning) Strix Halo features a **50 TOPS XDNA 2 NPU** (`/dev/accel/accel0`). Extensive empirical benchmarking demonstrates: - **Sustained Decode Ceiling:** **33.8 tok/s** via embedded MTP (iGPU only) is the practical ceiling. The NPU does *not* improve sustained decode speed. - **Proven NPU Value:** **1.8× faster first-token TTFT on long prompts (870 ms vs 1587 ms)** via hybrid NPU-burst → iGPU handoff (`scripts/run_pipeline.py`). - **See `docs/NPU_INTEGRATION.md`** for complete setup instructions.
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View details ↗Qwen3.8-Flash-Next-IQ4_XS-GGUF
julianmbsecond-generation quant: same trunk type (iq4_xs), but calibrated with a 926-entry imatrix (1,024 chunks) via the ROCmFPX banded quantizer, and the 51B PLE lookup table **left at q8_0** (no `--tensor-type` cut). 5.56 bpw, 115 giB — 24 giB bigger than the 91g PLE file. pick M2 when you want the **best measured quality** and don't mind the 24 giB: at ≤32k plain it matches PLE, and at 32k MTP it measured fastest. for shallow-depth MTP speed take the 91g PLE; for deep 128k+ MTP the static 116g had a small in-sweep edge (17.4 vs 13.3/13.5 — within the same-config spread, n=1 caveat). every quant descends from an F16 that was byte-verified against the official `Qwen/Qwen3.8-Flash-Next-FP8` checkpoint: hyper-connection norms folded to (1 + w) (97/97 tensors — the converter bug that produces deterministic garbage is fixed in our pipeline), PLE fp8 scale applied, expert stacking identity probed 512x3, GDN v-head reorder checked. details: https://github.com/julianmb/haloq38flash
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View details ↗NovaAnimeXLV110
JulianoG121基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗bert-xsmall-dummy
julien-c基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗DPRNNTasNet-ks16_WHAM_sepclean
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View details ↗dummy-diff-tokenizer
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View details ↗dummy-unknown
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View details ↗EsperBERTo-small
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View details ↗fasttext-language-id
julien-c> [1] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, Bag of Tricks for Efficient Text Classification > [2] A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, T. Mikolov, FastText.zip: Compressing text classification models
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View details ↗hotdog-not-hotdog
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View details ↗Qwen3.5-4B-uncensored-GGUF
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View details ↗skops-digits
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View details ↗Soar-qwen-14b
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