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flint-cmf

infosave

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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This repository contains all three dense reasoning models from IBM's Granite 4.2 family: 3B, 8B and 30B, each converted directly from the original bf16 checkpoint to Q4TP and Q8_2F CMF. A CMF is one memory-mapped file containing the weights, tokenizer, exact chat template and integrity hashes. The same artifact runs on CPU, Vulkan (NVIDIA/AMD/Intel), DX12 and Metal; inference needs no Python, PyTorch, CUDA toolkit or model-specific GPU build. Use `--no-think` for a direct answer. Without it, the embedded Granite template opens the model's native ` ... ` reasoning block. Numbers are single-stream `cortiq bench --tokens 100 --core --ignore-eos` steady decode on an NVIDIA A40. They exclude model load and prompt prefill. The 3B Q4TP run prefills at 80.3 tok/s and reaches first token in 0.58 s; its Q8_2F control prefills at 47.5 tok/s and reaches first token in 0.93 s. The 30B Q8_2F file is larger than the A40's safe physical graph live set: Cortiq automatically kept 55 of 64 layers on Vulkan and ran the nine-layer tail on the host. That 100-token run peaked at 43,036 MiB of the A40's 46,068 MiB and completed without steady-state weight uploads.

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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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Hy-MT2-cmf

infosave

Tencent's Hy-MT2 translation family — 33 languages, instruction-following translation (terminology, style, placeholders, structured data) — as CMF files for `cortiq`: one memory-mapped file per model with the weights, the vendor tokenizer, the exact chat template and per-tensor hashes. The same file runs on the CPU, on Vulkan (NVIDIA / AMD / Intel), DX12 and Apple Metal; no Python, no PyTorch, no CUDA toolkit. **Requires cortiq 0.7.0 or newer.** Earlier runtimes do not know the `hunyuan_v1_dense` / `hy_v3` architectures (and 0.6.9 crashes on any 4-bit embedding — fixed in the same release). ¹ Twelve 512-token windows of wikitext-2 test, exact attention, the same yardstick every card on this account uses. The bf16-class reference for the 1.8B (`q8_2f`, not published) scores 24.77 — the 4-bit file sits within 10% (27.13 vs 24.77) of it. These are translation models: their perplexity on English encyclopedia prose is not what they were trained for, which is why column ² exists.

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LFM2.5-cmf

infosave

LiquidAI's LFM2.5 is a hybrid: most layers mix with a short gated convolution and only a few carry full attention, so the state a token needs is small and constant where the convolution runs. These are those checkpoints in the CMF container — a single memory-mapped file read by `cortiq`, a Rust binary with no ML framework under it. GPU via Vulkan/Metal/DX12 with a CPU fallback; NVIDIA, AMD, Intel and Apple silicon read the same file. All three are 4-bit tiled with ladder scales (`q4tp`), quantized straight from the bf16 checkpoints. Steady-state decode, `cortiq bench --core`, single stream, cortiq **0.5.99+** (the whole-token graph learned this family's short-conv mixer and its sigmoid-routed MoE in 0.5.98/0.5.99 — earlier versions decode it an order of magnitude slower).

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> **Model release candidate.** All three weight files are uploaded and SHA-256 verified. > Text and greedy MTP checks pass; strict multimodal acceptance is still incomplete. > **Engine: Cortiq 0.7.7** · crates.io. Older published versions do not include this MiMo integration. **These are two complete file sets, not two copies of the 164-GB backbone.** Full includes the same text weights plus a **1.241-GB multimodal companion**. To upgrade from Text-only, download only the `.mm.cmf` file into the same folder. Text-only does not download or load the media towers. MiMo is a mixture-of-experts model with approximately **310 billion total parameters**, not a 15-billion-parameter checkpoint. Only a subset (roughly 15 billion parameters) is active per token, but **all experts must be stored**. Even a hypothetical uniform four-bit representation of 310 billion parameters would occupy about **155 GB**, before scales, metadata and higher-precision tensors. This conversion intentionally retains q8 and float tensors where needed.

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Nanbeige/Nanbeige4.2-3B converted to the CMF format: a single mmap-served file executed by a pure-Rust runtime. Nanbeige 4.2 is a **Looped Transformer**: 22 physical layers applied twice (44 virtual layers) with a re-norm at each loop boundary — the engine executes the loop natively — and a reasoning model that thinks before it answers. Measured on a **fanless MacBook Air (Apple M4)** — 10-core CPU (4 performance + 6 efficiency), 10-core GPU, 24 GB unified memory, macOS 27. No external cooling, no overclock, nothing plugged in.

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COST EXPLORER

好模型，也要算好成本。

费用分析

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

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

选型与常见问题
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。

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