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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 84991
This model is a fine-tuned version of Wav2Vec2-BERT 2.0 for Kinyarwanda automatic speech recognition (ASR). It was trained on the 1000 hours dataset from the Kinyarwanda ASR hackthon on Kaggle (Track B), dataset covering Health, Government, Finance, Education, and Agriculture domains. The model is robust and the in-domain WER is below 8.4%. - **Developed by:** Badr al-Absi - **Model type:** Speech Recognition (ASR) - **Language:** Kinyarwanda (rw) - **License:** CC-BY-4.0 - **Finetuned from:** facebook/w2v-bert-2.0 - **Repository:** https://huggingface.co/badrex/w2v-bert-2.0-kinyarwanda-asr - **Dataset:** Kinyarwanda ASR Track B
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查看详情 ↗BTL-4-Compact
badtheorylabs**The whole 35B model in a single 9.96 GB file.** 2.30 bits per weight, and it retains 94.1% of the full-precision model's measured behaviour. BTL-4 is a mixture of experts with roughly 2.1B active parameters per token, so it costs a large model's memory and a small model's compute. Compact is the edition that runs on hardware you already own — one file, one command, a running agent. No base download, no reconstruction. Loads in llama.cpp, Ollama and LM Studio.
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查看详情 ↗GLM-5.3-Flash-Uncensored-VQ-GGUF
Baekpica> [!WARNING] > **Experimental low-bit release.** The routed experts, which hold most of the > weights, are stored at 1.625 or 2.625 bits per weight depending on the layer > (about 2.1 on average), far below common 4-bit GGUF quants. You may see > failures the source model rarely shows: reasoning that loops or never > reaches an answer, text that breaks mid-character (shown as `�`) or switches > language mid-word, and weaker answers in general. This is exploratory > quantization research, not a drop-in replacement for the source model. > > **The files can change.** This line of conversions is still being researched > (as of 2026-10-21, weighted Lloyd fitting of the codebooks is being tested), > so any file here may be replaced by a new revision. Replacements so far: two > since the first release on 2026-10-17. The 8-bit residual stage that > protects sensitive routed layers was added to layers 3–16 and then widened > to 19 layers (2026-10-18). The earlier files remain at the tags `q4k-v3` and > `r13p8-l3-16`. To keep a fixed build, download a pinned commit: > `hf download Baekpica/GLM-5.3-Flash-Uncensored-VQ-GGUF --revision `. > > **Problems or odd output?** Feel free to post them in the > Community tab, > even as rough notes. The prompt, sampling settings and runtime commit help > most. > **Status, 2026-10-18:** the files are the 19-layer R13+8 revision: the > residual stage now covers routed layers 3–17, 22, 27, 41 and 44 (before: > 3–16). Loading needs a ds4-dfm-rs build with GVQ1 container v2 support > (`main` since PR #96, > merge `34c46d18`); the runtime binds the residual stage on any routed layer, > so no runtime change is needed. Speed and memory of this revision on DGX > Spark are not measured yet. Previous revisions are kept at the tags > `r13p8-l3-16` > (R13+8 on layers 3–16) and > `q4k-v3` > (all routed layers R13). Experimental custom GVQ1 / GGUF v3 conversion of orcarouter/GLM-5.3-Flash-Uncensored-FP8 (`150bd5e3a957db747db1224cc4f8891bcfa7a7f0`), the abliterated (refusal-removed) build of Z.AI GLM-5.3-Flash in the official block-FP8 layout, for DGX Spark (GB10) with ds4-dfm-rs. Standard llama.cpp/Ollama/Transformers loading is not claimed.
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Baekpica> [!WARNING] > **Experimental low-bit release.** The routed experts, which hold most of the > weights, are stored at 1.625 or 2.625 bits per weight depending on the layer > (about 2.1 on average), far below common 4-bit GGUF quants. You may see > failures the source model rarely shows: reasoning that loops or never > reaches an answer, text that breaks mid-character (shown as `�`) or switches > language mid-word, and weaker answers in general. This is exploratory > quantization research, not a drop-in replacement for the source model. > > **The files can change.** This line of conversions is still being researched > (as of 2026-10-21, weighted Lloyd fitting of the codebooks is being tested), > so any file here may be replaced by a new revision. Replacements so far: > three since the first release on 2026-10-15. The non-expert weights were > requantized (Q4_K v3, 2026-10-16), then the 8-bit residual stage that > protects sensitive routed layers was added to layers 3–16 and widened to 19 > layers (2026-10-18). The earlier files remain at the tags `vq-original`, > `q4k-v3` and `r13p8-l3-16`. To keep a fixed build, download a pinned commit: > `hf download Baekpica/GLM-5.3-Flash-VQ-GGUF --revision `. > > **Problems or odd output?** Feel free to post them in the > Community tab, > even as rough notes. The prompt, sampling settings and runtime commit help > most. > **Status, 2026-10-18:** the files are the 19-layer R13+8 revision: the > residual stage now covers routed layers 3–17, 22, 27, 41 and 44 (before: > 3–16). Loading needs a ds4-dfm-rs build with GVQ1 container v2 support > (`main` since PR #96, > merge `34c46d18`); the runtime binds the residual stage on any routed layer, > so no runtime change is needed. Speed and memory of this revision on DGX > Spark are not measured yet. Previous revisions are kept at the tags > `r13p8-l3-16` > (R13+8 on layers 3–16) and > `q4k-v3` > (all routed layers R13). Experimental custom GVQ1 / GGUF v3 conversion of the official Z.AI GLM-5.3-Flash (`91b69215804f2d2036a8867f6ce44848e278dcda`) for DGX Spark (GB10), run by ds4-dfm-rs. Standard llama.cpp/Ollama/Transformers loading is not claimed.
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查看详情 ↗MiMo-V2.6-Flash-RL-GGUF
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查看详情 ↗> **Mixed weights with original-representation calibration.** The four-shard language model passed its 508-tensor structural and quantization audit. > > **Role-aware precision:** expert gate/up **IQ2_XXS**, expert down **IQ2_XS**, shared dense paths and embedded MTP **Q8_0**, media **BF16**, and numerical controls **F32**. > > **Measured GGUF storage: 93.092 GB / 86.699 GiB**, including main model, multimodal projector and separate DFlash weights. Auxiliary files and runtime memory are additional. > > Text, image, audio, video and audiovisual assets are retained. **ds4-dfm-rs on one DGX Spark GB10:** the bounded serving checks below cover two 256K text banks, serial media, live partial reuse and restart disk KV with MTP off. Prior 512K serial text and 256K serial media/DFlash checks used different configurations. The checks do not establish throughput parity or general quality retention. The 256K mixed setup uses `--max-seqs 2 --prefix-reuse partial --kv-disk-dir ... --mtp-mode off`, `DS4_MIMO2_PREFILL_CHUNK=2048`, and a VMM owner with `--no-repack-q8-aligned`. Aligned IQ2 expert weights stay enabled; disabling the optional Q8 repack saves 6.26 GiB. The two text banks hold separate KV and serialize forward passes through shared scratch. Images, video and audio use the serial lane. These are bounded functional checks, not a full-length 256K mixed request, multimodal quality, cache reuse at 1M, or throughput qualification. The serving report records the exact limits. Embedded MTP without DFlash and the external five-layer DFlash with serial media were checked separately before this setup.
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查看详情 ↗MiniMax-M3.1-Flash-VQ-GGUF
Baekpica> [!WARNING] > **Problems or odd output?** Post them in the > Community tab, even as > rough notes. The prompt, sampling settings and runtime commit help most. **MiniMax's 428B native multimodal MoE with its full 1,048,576-token context, its sparse attention, its vision/video tower and its DSpark draft head, sized for one 128 GB DGX Spark.** The 236 GB checkpoint (NVFP4 experts, MXFP8 attention) becomes **115.5 GB** of GGUF parts, of which **111.3 GB (103.7 GiB) stay resident**; the BF16 token table and the vision tower are read from SSD, and the attention K/V of a long session lives on SSD, addressed block by block by the model's own MSA indexer. > **Runtime support, 2026-10-24:** runs on one DGX Spark with the ds4-dfm-rs `minimax_m3_vq` family > (merged PR #110): text, images and video, the DSpark draft head and the native context. > **Performance, 2026-10-29:** matched 2K–64K Prefill **1,001.85 → 1,020.29 tok/s (+1.84%)**; > the last 2K at 64K reaches **990.08 tok/s (+2.87%)**, using the runtime changes in > PR #126 (conditions and Decode limits below). > The historical 1,048,319-token prompt prefilled at 580 tok/s and > decoded. Converted 2026-10-23: 61 independent GGUF parts (115,526,325,376 bytes), every payload > hash in `tensor-manifest.json`, all 89,729 source tensors accounted for; codebooks fitted with > calibration statistics from 4.18M tokens (text, images and video) run through the original > checkpoint. **Measured against the original, teacher-forced on held-out chats: KL 0.150 and the > same next token 88.2% of the time on assistant turns** (the original batched differently: KL > 0.003). The memory and kernel budgets below are the design computed from exact byte layouts.
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价格待核验多模态与渠道费用须分别核对
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。