occamy-1.0-NVFP4
Accio-Lab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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요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗DISCOVER & COMPARE
AI 모델 탐색 5932
Qwen3.8-27B-Q3_K_M-GGUF
aclava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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자세히 보기 ↗vit-base-nsfw-detector
AdamCoddThis model is a fine-tuned version of vit-base-patch16-384 on around 25_000 images (drawings, photos...). It achieves the following results on the evaluation set: - Loss: 0.0937 - Accuracy: 0.9654 ** New [07/30] **: I created a new ViT model specifically to detect NSFW/SFW images for stable diffusion usage (read the disclaimer below for the reason): **AdamCodd/vit-nsfw-stable-diffusion**. **Disclaimer**: This model wasn't made with generative images in mind! There is no generated image in the dataset used here, and it performs significantly worse on generative images, which will require another ViT model specifically trained on generative images. Here are the model's actual scores for generative images to give you an idea: - Loss: 0.3682 (↑ 292.95%) - Accuracy: 0.8600 (↓ 10.91%) - F1: 0.8654 - AUC: 0.9376 (↓ 5.75%) - Precision: 0.8350 - Recall: 0.8980
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자세히 보기 ↗nex-agi_Nex-N2-mini-GGUF
adamm-hf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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자세히 보기 ↗biomed-gemma-3-4b-it
AdaptLLM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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자세히 보기 ↗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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요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗This is a multimodal, refusal-ablated (commonly described as "uncensored") W4A4 NVFP4 derivative of the reproducible `trohrbaugh/Qwen3.8-27B-heretic-ara` BF16 checkpoint. It is intended for native Blackwell NVFP4 inference. This artifact passed its text-capability, image-vision, benign refusal-surface, native MTP-acceptance, integrity, and clean-load gates on vLLM 0.23. See `BUILD_MANIFEST.json`, `VALIDATION_REPORT.json`, and `SHA256SUMS` for exact provenance and results. Video tensors/processors are preserved, but video input was not part of the live runtime gate. The live gate used `Qwen3_5ForConditionalGeneration`, native three-token MTP, the FlashInfer CUTLASS NVFP4 kernel, an 8,192-token context, and an RTX PRO 6000 Blackwell GPU. Loaded model memory was approximately 19.53 GiB.
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자세히 보기 ↗fuyu-8b
adeptWe’re releasing Fuyu-8B, a small version of the multimodal model that powers our product. The model is available on HuggingFace. We think Fuyu-8B is exciting because: 1. It has a much simpler architecture and training procedure than other multi-modal models, which makes it easier to understand, scale, and deploy. 2. It’s designed from the ground up for digital agents, so it can support arbitrary image resolutions, answer questions about graphs and diagrams, answer UI-based questions, and do fine-grained localization on screen images. 3. It’s fast - we can get responses for large images in less than 100 milliseconds. 4. Despite being optimized for our use-case, it performs well at standard image understanding benchmarks such as visual question-answering and natural-image-captioning. Please note that **the model we have released is a base model. We expect you to need to finetune the model for specific use cases like verbose captioning or multimodal chat.** In our experience, the model responds well to few-shotting and fine-tuning for a variety of use-cases. Architecturally, Fuyu is a vanilla decoder-only transformer - there is no image encoder. Image patches are instead linearly projected into the first layer of the transformer, bypassing the embedding lookup. We simply treat the transformer decoder like an image transformer (albeit with no pooling and causal attention). See the below diagram for more details.
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자세히 보기 ↗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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자세히 보기 ↗DeepSeek-V4.1-Flash-UNCENSORED-GGUF
Adolphsson**Uncensored 552B MoE with 1M-token context, vision, and full tool/reasoning support — converted to GGUF for llama.cpp.** Built from `dealignai/DeepSeek-V4.1-Flash-UNCENSORED-FP8` (permanent weight-level abliteration of `deepseek-ai/DeepSeek-V4.1-Flash`), converted to GGUF and requantized. **No guardrails: the model follows on prompts the base model refuses (verified, see Benchmarks).** > ## ✅ Update 2026-10-15 — corrected re-quants are live > > **The Q3_K_M and Q4_K_M files on the Hub have been replaced** with corrected re-quants built from the Q8_0 bridge using the corrected per-layer ramp (see below). If you downloaded Q3 or Q4 earlier, the files have changed — re-download. > > - **Q3_K_M:** now **352 GB (9 × ≤50 GB shards)** — 23 GB smaller than the previous build, identical measured quality at every benchmark we ran on both. > - **Q4_K_M:** now **426 GB (3 shards)** — routed experts are pure Q4_K as the tier name says (the previous build re-encoded the checkpoint-native MXFP4 experts into Q4_K/Q5_K, which is what the audit flagged). > - **Q2_K:** rebuilt (v3) — now pins the indexer/hyperconn layers per the recipe (the previous build left them at base precision). Same size class, files replaced on the Hub. The benchmark table below is measured on v3; the 1M NIAH tier on v3 is running. > - **Q8_0:** unchanged — still the reference bridge. > > Details: Per-group quantization & size · Benchmarks
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자세히 보기 ↗GLM-5.3-Flash-UNCENSORED-MTP-Maya-L-GGUF
Adolphsson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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자세히 보기 ↗Qwen3.5-35B-A3B-GGUF
Advantech-EIOT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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자세히 보기 ↗MonkeyOCRv2-B-GGUF
aenawi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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자세히 보기 ↗> **The near-lossless Apple-Silicon build** of `AEON-7/Gemma-4-12B-it-AEON-Abliterated-K4-BF16`. Every language-decoder linear is `mxfp8` (8-bit, group 32); the tied soft-capped head and the vision/audio projectors are kept at **bf16**. Built and validated on a **MacBook Pro M4 Pro (48 GB)**. > > **Target hardware: Apple Silicon (M-series), best on 24 GB+ unified memory.** Full multimodal (text + image + audio) via `mlx-vlm`. > > Want the smallest build for a 16 GB Mac? See the compact FP4 sibling: `…-MLXFP4`. This is the **maximum-fidelity** member of the MLX quant grid: measured **top-1 token agreement 0.924 and median KL ≈ 0.002 nats** against the BF16 source on the model's own greedy output — below the perceptual/sampling-noise floor on the typical token. **Near-lossless**, and the recommended build when quality is paramount. **0 → running on a fresh Mac** (no Python, no tools needed) — `uv` installs a correct Python + the deps for you:
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요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗> **The compact, fast, 16 GB-friendly Apple-Silicon build** of `AEON-7/Gemma-4-12B-it-AEON-Abliterated-K4-BF16`. True 4-bit `mxfp4` on the bulk of the decoder, **8-bit `mxfp8` on the two residual-writers that carry the abliteration edit**, and **bf16** on the soft-capped head and the vision/audio projectors. Built and validated on a **MacBook Pro M4 Pro (48 GB)**. > > **Target hardware: Apple Silicon (M-series), runs on 16 GB+ unified memory** (peaks ~10.1 GB). Full multimodal (text + image + audio) via `mlx-vlm`. > > Want maximum fidelity? See the near-lossless 8-bit sibling: `…-MLX-8bit`. This is the **smallest** member of the MLX quant grid (9.3 GB) and the **fastest single-stream** — a high-quality compact build for Apple Silicon on a tight unified-memory budget. It stays fully coherent, keeps the abliteration intact (**0/8 refusals on the harmful probe**), and preserves the full vision/audio path. For the tightest possible match to BF16, the near-lossless **MLX-8bit** sibling is one click away. **0 → running on a fresh Mac** (no Python, no tools needed) — `uv` installs a correct Python + the deps for you:
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요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗> [!IMPORTANT] > **Superseded by Qwen3.8 — use this instead:** `AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-NVFP4-MIXED` > > **Operator recipes (Spark Dynamic DFlash lattice, TP=2, RTX):** `AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED` > > This Qwen3.6-27B card stays online for history and existing downloads. For new work, move to the Qwen3.8 Ultimate Uncensored NVFP4-MIXED release. That line is far superior in capability and reaches substantially more of what the architecture can do, with a more refined mixed quantization methodology (NVFP4 + FP8 lattice) than these older Qwen3.6 exports. > > Serve with the unified container: `ghcr.io/aeon-7/aeon-vllm-ultimate:latest` (Spark / GB10) or `ghcr.io/aeon-7/aeon-vllm-ultimate-rtx:latest` (RTX discrete). > **The flagship, max-fidelity Apple-Silicon build** of `AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16`. Affine **8-bit** (group-64) on the compressible bulk of the hybrid decoder, and **BF16** on the parts that don't tolerate it: the Gated-DeltaNet / Mamba state dynamics, the full vision tower, and the in-weights MTP head. Built and validated on a **MacBook Pro M4 Pro (48 GB)**. > > **Target hardware: Apple Silicon (M-series), 36 GB+ unified memory** (peaks 29.85 GB). Full multimodal (text + image) via `mlx-vlm`. > > Want a smaller, faster build? See the compact sibling: `…-Multimodal-MLX-FP4` (16 GB, 1.85× faster decode). This is the **fidelity** member of the MLX quant grid (29.5 GB on disk, 8.634 bpw) — the tightest match to the BF16 source you can run natively on Apple Silicon. Affine 8-bit is near-lossless on the residual-writers, and the recipe keeps the numerically fragile **Gated-DeltaNet** recurrence, the entire **vision tower**, and the **MTP head** in BF16 so the hybrid SSM, multimodal path, and self-speculation all stay intact. For a tight unified-memory budget (24 GB+ Macs) at ~3.2× the single-stream throughput, the compact **MLX-FP4** sibling is one click away.
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요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗> [!IMPORTANT] > **Superseded by Qwen3.8 — use this instead:** `AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-NVFP4-MIXED` > > **Operator recipes (Spark Dynamic DFlash lattice, TP=2, RTX):** `AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED` > > This Qwen3.6-27B card stays online for history and existing downloads. For new work, move to the Qwen3.8 Ultimate Uncensored NVFP4-MIXED release. That line is far superior in capability and reaches substantially more of what the architecture can do, with a more refined mixed quantization methodology (NVFP4 + FP8 lattice) than these older Qwen3.6 exports. > > Serve with the unified container: `ghcr.io/aeon-7/aeon-vllm-ultimate:latest` (Spark / GB10) or `ghcr.io/aeon-7/aeon-vllm-ultimate-rtx:latest` (RTX discrete). > **The compact, fast, MTP-accelerated Apple-Silicon build** of `AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16`. True 4-bit `mxfp4` on the bulk of the hybrid decoder, **8-bit affine islands on the sensitive GQA k/v + embedding + head**, and **bf16** on the Gated-DeltaNet state dynamics, the vision tower, and the MTP head. Built and validated on a **MacBook Pro M4 Pro (48 GB)**. > > **Target hardware: Apple Silicon (M1+), runs on 24 GB unified memory** — 16 GB on disk, ~17 GB peak. **15.2 tok/s single-stream, 26.5 tok/s with MTP self-speculation (1.78× lossless).** Full multimodal (text + image) via `mlx-vlm`. > > Want maximum fidelity? See the near-lossless 8-bit sibling: `…-MLX-8bit`. This is the **smallest** member of the MLX quant grid (16 GB) and the **fastest single-stream** — a high-quality compact build for Apple Silicon that fits a **24 GB** Mac and, with its native MTP drafter, decodes at **26.5 tok/s** — roughly **3.2× the 8-bit's 8.2 tok/s**. It stays fully coherent, keeps the abliteration intact, and preserves the full vision path. For the tightest possible match to BF16, the near-lossless **MLX-8bit** sibling is one click away.
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자세히 보기 ↗> ✅ **Validated on the unified AEON vLLM Ultimate image `ghcr.io/aeon-7/aeon-vllm-ultimate:latest`** (= `:2026-06-18-v0.23.0-dflashfix`; rollback `:2026-06-11-pr41703`) — vLLM 0.23.0 built from source for GB10 / sm_121a + the AEON DFlash stack. Loads + serves cleanly (compressed-tensors MoE) — **including working multimodal image understanding** (a vision tensor-prefix bug was fixed 2026-06-18 — see the **Vision fix** section below) — and with DFlash `num_speculative_tokens: 11` now scales to **c=64** with **up to ~740 tok/s aggregate** and **no concurrency crash**. This single image supersedes the older `vllm-spark-omni-q36:v1.2` container. **Recommended container base.** > **🚀 PRODUCTION DEPLOYMENT GUIDE: github.com/AEON-7/Qwen3.6-35B-A3B-heretic-NVFP4-DFlash** > > The GitHub repo is the **definitive turn-key setup** for DGX Spark — end-to-end > deployment guide, validated OpenClaw config, and a concurrency-sweep benchmark harness. > > - Image: `ghcr.io/aeon-7/aeon-vllm-ultimate:latest` (vLLM 0.23.0 source-built for sm_121a + the AEON speculative-decoding stack — see What we fixed for the DGX Spark) > - Pairs with `z-lab/Qwen3.6-35B-A3B-DFlash` drafter (must be **post 2026-04-19** revision) > - **Production-stable** under sustained chat load and concurrent agent fleets up to c=64 on DGX Spark v1 of this checkpoint had `model.language_model.layers.X.*` keys remapped to `model.layers.X.*` so vLLM's text-only `Qwen3_5MoeForCausalLM` loader would pick them up. That layout was unstable in production — intermittent NaN/crash in the prefix-strip codepath during real chat sessions.
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자세히 보기 ↗Atlas-2.1-Small
AeroAIAviation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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자세히 보기 ↗Qwen3.8-Flash-Next-GGUF
AetherKelly00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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자세히 보기 ↗> **Updated 2026-10-13: both files.** Same file names, sizes and tensor types, better weights from an improved encode. This is huihui-ai/Huihui-Qwen3.8-Flash-Next-abliterated encoded the same way as our Gyro files. Abliteration is huihui-ai's work: it removes the refusal direction from the model's weights. We only quantized it. **Which setup fits your hardware, NVIDIA and AMD instructions, context limits, vision and speculative decoding are all on the main Gyro card and apply unchanged here; only the file names differ.**
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자세히 보기 ↗Qwen3.8-Flash-Next-Gyro-GGUF
agentionai**Qwen3.8-Flash-Next (125B MoE) on one GPU: 27.6 GiB for a 32 GB card, 35 GiB for 48 GB, 50 GiB for 64 GB.** The best quality per GiB at every size we ship. Gyro-S fits a 32 GB card: 27.64 GiB of weights, 28.8 GiB at 64k context, up to 128k context. Coding on a 24 GB card: agentionai/Qwen3.8-Flash-Next-Gyro-Coder-GGUF. Abliterated Gyro-S and Gyro-M: agentionai/Qwen3.8-Flash-Next-Gyro-abliterated-Huihui-GGUF. The routed experts are stored in a rotated basis with our own rotor code (Agention Precision Rotor, APR). The `TQ1_0` / `TQ2_0` / `TQ3_0` in the names is only a size class for the Hub (the experts use the trellis types TQK6/TQK7, TQ2_T and TQP5-7). **Upstream llama.cpp cannot load these files yet.** They run in:
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자세히 보기 ↗Signal-3.8-27B
agentionai> [!IMPORTANT] > **Update 2026-09-13 — use these settings for stable, short reasoning** > Temperature 0.6, min-p 0.05 (top-p 0.95, top-k 20), KV cache q8_0 or f16, and the MTP draft head on. > Without min-p, long answers can run away into repetition on some draws; a 4-bit value cache makes that > deterministic. Both are fixed by the settings above, on Signal and on the base model alike. > [!WARNING] > The weights in this repository were updated on 2026-09-13 (the GGUF tiers were rebuilt from them). The first release's tiers could, rarely, > repeat an answer line when sampling without the draft head; the rebuilt tiers showed no loop or cap > in 55 traces under the same conditions. If you downloaded before that date, re-download. AgentionAI Signal is a minimally invasive fine-tune of Qwen3.8-27B designed for lower generation latency and better token efficiency. On our held-out general-prompt evaluation, Signal produces **57% fewer answer tokens** and uses **52% fewer thinking tokens**, while matching or improving the measured answer quality of the base model.
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자세히 보기 ↗Signal-3.8-27B-GGUF
agentionai> [!IMPORTANT] > **Recommended settings** > Temperature 0.6, top-p 0.95, top-k 20, min-p 0.05, **presence_penalty 1.0**, KV cache q8_0 or f16. > Keep min-p and presence_penalty on: without them, long generations can run into repetition on some > draws (a 4-bit value cache makes that deterministic). If you enable the MTP draft head on recent > mainline llama.cpp, also pass **`--no-spec-draft-backend-sampling`** — the default backend draft > sampling does not apply your sampler to accepted draft tokens, which can let repetition run away > regardless of the settings above. > [!WARNING] > The tiers in this repository were rebuilt on 2026-09-13. The first release's tiers could, rarely, > repeat an answer line when sampling without the draft head; the rebuilt tiers showed no loop or cap > in 55 traces under the same conditions. If you downloaded before that date, re-download. AgentionAI Signal is a minimally invasive fine-tune of Qwen3.8-27B designed for lower generation latency and better token efficiency. On our held-out general-prompt evaluation, Signal produces **25% fewer answer tokens** and uses **41% fewer thinking tokens**, while matching or improving the measured answer quality of the base model.
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