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AI モデル 84759
- ThinCtx-Qwen3.5-4B-i1-GGUF↗
mradermacher
- XORTRON-CriminalComputing-RICO-v4-GGUF↗
mradermacher
- Ornith-1.5-35B-A3B-GGUF↗
na0x2c6
GGUF quantizations of ornith-ai/Ornith-1.5-35B-A3B, made with an importance matrix, plus a KL-divergence comparison with the other published GGUFs of this model. > [!NOTE] > These files were made for a quantization experiment and are evaluated only by the KLD measurements below, not by task benchmarks, long-context use or speed tests. Check them on your own workload before relying on them. The four "comparison only" files are there so that the effect of `-hq` in the tables below can be checked; at each size the `-hq` file is better. All files are Q4_K_M with up to two kinds of changes. The `-hq` part raises the tensors outside the routed experts to Q8_0: attention, the linear-attention (SSM) projections, the shared expert, and the MTP projection. The `-exps` part changes the routed expert tensors (`ffn_{gate,up,down}_exps`) in layers 0-39. The token embedding and the output keep the Q4_K_M types (Q4_K and Q6_K). All files keep the built-in MTP (NextN) layer, so they work with `--spec-type draft-mtp`. For image input, use the vision projector from the official repo (`mmproj-Ornith-1.5-35B-BF16.gguf`).
- frisky-90m↗
netsrik
- Cosmos-Reason2-2B↗
NVIDIA
- KVzap-linear-Qwen3-32B↗
NVIDIA
- lahgtna-omnivoice-v2↗
oddadmix
- Ornith-1.5-35B-MaxQuality-MTP-GGUF↗
offmonreal
- GPT-5.6 Terra (Low)↗
OpenAI
- MiniCPM-V-4.6-BNB↗
openbmb
> **This repository hosts the bitsandbytes (NF4, 4-bit) quantized version of MiniCPM-V 4.6.** For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-V-4.6. A Pocket-Sized MLLM for Ultra-Efficient Image and Video Understanding on Your Phone * [2026.05.17] ⭐️⭐️⭐️ We release the API service of MiniCPM-V 4.6, with a **public free API key** together! Try it now.