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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 27
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查看详情 ↗> **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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查看详情 ↗> **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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查看详情 ↗Complete copy-paste recipe — pull the container, pull the model (fresh), pull the DFlash drafter (fresh), then serve with the vetted DGX Spark recipe (DFlash n=10, drafter `flash_attn`, body `triton_attn`). The recipe notes below cover tag-specific drafter backends and the production GPU-util / context-tier profile; see also Container Image Details. **Recipe notes — the drafter backend is tied to the image tag:** - **On `:latest` / `:2026-07-01-v0.24.0` (current; same behavior as the `:2026-06-18-v0.23.0-dflashfix` fix lineage):** the drafter **must** use `attention_backend: flash_attn` (enabled for multimodal Gemma targets by PR #41703's `use_mm_prefix=False`). `flex_attention` **crashes at the first request** on this image (non-contiguous KV view in the new KV-sharing path). `--enable-prefix-caching` is safe and recommended — soak-validated with DFlash (see the fixes section below). - **On the rollback tag `:2026-06-04-pr44389` (pre-fix):** the reverse — only `flex_attention` loads (`flash_attn` fails with `partial multimodal token full attention not supported`), the drafter silently runs without sliding-window support (~0% acceptance beyond 2k-token contexts), and `--enable-prefix-caching` triggers a slow acceptance collapse (see below). Not recommended. - Either way the body stays on `triton_attn` (Gemma's heterogeneous head dims), and do **not** set `--kv-cache-dtype fp8` — DFlash's non-causal drafter requires BF16 KV. `--reasoning-parser gemma4` cleanly splits `enable_thinking` output into `reasoning_content`. `num_speculative_tokens=10` is the value our production fleet runs; the drafter's trained block size is 16 and z-lab ships 15 — an n=10-vs-15 re-bench on the fixed drafter is pending (the earlier "10 beats 15" result was measured on the pre-fix drafter and shouldn't be treated as settled). The model's `config.json` already ships the vision ignore-list fix (`re:.*embed_vision.*`, `re:.*vision_tower.*`) so vLLM keeps the vision tower BF16. This default profile suits agentic gateways — one large full-context chat plus many short-lived subagents under the `--max-num-seqs 64` cap. For short-context throughput benchmarking, use `--max-model-len 32768 --max
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查看详情 ↗> [!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). > **Deployment, operations & benchmarks → github.com/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-DFlash** > > The GitHub repo is the source of truth for the production deployment guide, hardware-tuned docker-compose configs (DGX Spark NVFP4, A100/H100 BF16), full configuration reference, measured throughput benchmarks, and `AGENTS.md` — an operator's manual that pre-empts common stale-documentation traps for AI coding agents working on this stack. > **🆕 2026-05-01 — MTP head grafted in.** This repo now ships with the original `mtp.*` head (15 tensors, ~0.85 GB) restored from the `Qwen/Qwen3.6-27B` base. vLLM's `--speculative-config '{"method":"qwen3_5_mtp","num_speculative_tokens":3}'` works directly on the BF16 checkpoint with no extra steps. Measured on DGX Spark: **mean accepted length 3.3/3, P0 ≈ 90% acceptance, avg draft acceptance 78%** — comparable to the base model, confirming abliteration doesn't damage the model's top-K distribution that MTP relies on. Credit to `@tcclaviger` for the empirical finding (discussion #6) that MTP doesn't need retraining post-abliteration. **No retraining was performed.** The MTP head is the unmodified base — only the residual-stream-writing weights of the LM were orthogonalized in the original v8 abliteration pass; the MTP head is independent of that pathway.
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查看详情 ↗> [!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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查看详情 ↗> [!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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查看详情 ↗> [!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). > **Deployment, operations & benchmarks → github.com/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-DFlash** > > The GitHub repo is the source of truth for the production deployment guide, hardware-tuned docker-compose configs, full configuration reference, measured benchmarks, and `AGENTS.md` — an operator's manual that pre-empts common stale-documentation traps. > **🙏 Reference recipe credit:** The modelopt + MTP graft pipeline used to build this variant is based on **sakamakismile**'s validated Qwen3.6-27B-NVFP4-MTP series (22K+ downloads). They worked out the modelopt config, the per-projection quantization choices, and the MTP-head graft technique on the un-abliterated base; we adapted the same recipe to AEON-Ultimate's abliterated weights. The reference benchmark numbers cited below are theirs. Full credit for the recipe → sakamakismile.
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查看详情 ↗> [!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). > 💎 **Want higher quality?** This is the **XS (smallest-footprint)** body. If you have the VRAM — it's only *slightly* larger — the full **Multimodal-NVFP4-MTP** body runs at **parity speed** with materially **higher quality-eval scores**, and is the recommended DGX Spark daily-driver. Stay on **XS** when footprint / VRAM is tight. > **Deployment, operations & benchmarks → github.com/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-DFlash** > > The GitHub repo is the source of truth for the production deployment guide, hardware-tuned docker-compose configs, full configuration reference, measured benchmarks, and `AGENTS.md` — an operator's manual that pre-empts common stale-documentation traps.
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查看详情 ↗> [!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). NVFP4 hardware-quantized release of `AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored`. Same model, same 0/50 refusal rate, same preserved capabilities — now compressed from **51 GB BF16** to **26 GB NVFP4** for native FP4 hardware throughput on DGX Spark / GB10 / Blackwell sm_121a. This release is **multimodal-preserved** (vision tower stays BF16 — text + image inference fully functional) and **hybrid-attention-preserved** (the 48 linear-attention / GatedDeltaNet layers stay BF16; FP4 only applies to the 16 full-attention layers' output projections + all MLPs).
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查看详情 ↗> ✅ **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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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。