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AI 模型 2119

• SambaLingo-Hungarian-Chat-GGUF↗

  mradermacher

• nomic-embed-vision-v1.5↗

  nomic-ai

  `nomic-embed-vision-v1.5` is a high performing vision embedding model that shares the same embedding space as nomic-embed-text-v1.5. The easiest way to get started with Nomic Embed is through the Nomic Embedding API. For more information, see the API reference

• FastSam-S↗

  qualcomm

• MiniMax-H3-fl2va-ref2va-b25-49-r1024-r0↗

  xtanqn

  The two official checkpoints are merged into a single **self-contained fused model** that carries both pipelines in one file: The fusion method is **neither training nor LoRA** — it is tensor-level weight fusion. Three headline tiers are provided, ordered by reference-injection strength (`b25-49 → r1024 → r0`; the further right, the stronger the reference capability and the closer to ref2va; the further left, the more fl2va's native orientation is preserved). There are also **AdaLN block-range variants b15 / b20 / b30** (see below; only the starting block differs), ordered along the "preserve fl2va quality + light reference" axis: > All three tiers share the same key layout and quantization format, and can be swapped/loaded at any time. Use `b25-49` for "maximum fl2va quality + a little reference"; use `r1024` for "balanced reference and quality"; use `r0` for "maximum reference, upper-bound comparison".

• Qwen3.6-27B-Omnimerge-v4-GGUF↗

  ManniX-ITA

  GGUF quantizations of `ManniX-ITA/Qwen3.6-27B-Omnimerge-v4` — the **MLP-passthrough** variant that defends against the Qwen3.6 think-policy fragility we discovered. Source dtype is BF16; this repo provides the standard bartowski quant ladder (F16 → IQ2_XXS) for `llama.cpp`. > **Source model:** `ManniX-ITA/Qwen3.6-27B-Omnimerge-v4` (BF16 weights, model card with full benchmarks and methodology). > **NOT** a quant of clean Qwen/Qwen3.6-27B — these GGUFs contain the v4 merge. > > **MTP companion (2× decode speedup):** weight-identical GGUFs with the MTP head retained for `llama.cpp --spec-type draft-mtp` self-speculative decoding are at `ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-MTP-GGUF`. Quality is statistically indistinguishable from this repo (HE 137/164 ↔ 137/164, GPQA 155/198 ↔ 154/198); aggregate decode is 2.0-2.3 × faster on a single 24 GB GPU. Use that repo for interactive / single-request workloads where latency matters. All quants made using imatrix with calibration data v5, the same calibration set bartowski uses for the Qwen3.6 base release — so quality fingerprints are directly comparable to bartowski's `Qwen_Qwen3.6-27B-GGUF` repo.

• whisper-base-ser-dusha↗

  waveletdeboshir

  Model was fine-tuned on full Dusha-crowd with * augmentations Time Shift, Time Masking and Colored Noise; * WeightedRandomSampler.

• wavlm-base-plus↗

  Microsoft

  The base model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. - 60,000 hours of Libri-Light - 10,000 hours of GigaSpeech - 24,000 hours of VoxPopuli

• Ternary-Bonsai-2-9B-mlx-2bit↗

  prism-ml

  > **PrismML-Eng/Bonsai-demo is the source of truth for running these models.** Where anything here disagrees with it, it is right. For an OpenAI-compatible server, run `python -m mlx_vlm.server --model bonsai2-9b-mlx` and send the sampling settings in each request. For CUDA, CPU, and llama.cpp on Metal, use the GGUF packs of the same weights: **Ternary-Bonsai-2-9B-gguf**.

• Ternary-Bonsai-2-27B-MTPLX-Optimized-Speed↗

  Youssofal

  **MTPLX: the fastest way to run Ternary Bonsai 2 27B on a Mac. Native multi-token-prediction speculative decoding on Apple Silicon, exact at any temperature.** Prism ML's Ternary Bonsai 2 27B is Qwen 3.8 27B rebuilt with ternary weights, and Prism ML reports it keeps 98.2 percent of the full model's benchmark average in 8.85 GB. On MTPLX it runs on GPU kernels written for it, with the Qwen 3.8 27B multi-token-prediction head drafting ahead. That makes it faster than the 4-bit Qwen 3.8 27B in half the memory. It reads images, and it runs on a Mac with 16 GB. Prism ML's own figure for Bonsai 2 27B on an M5 Max with MLX is 46.8 tok/s. On MTPLX the same chip decodes it at 64.4 tok/s after a 4,061-token prompt.

• InternVL2_5-8B↗

  OpenGVLab

  We are excited to introduce **InternVL 2.5**, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model architecture while introducing significant enhancements in training and testing strategies as well as data quality. As shown in the following figure, InternVL 2.5 retains the same model architecture as its predecessors, InternVL 1.5 and 2.0, following the "ViT-MLP-LLM" paradigm. In this new version, we integrate a newly incrementally pre-trained InternViT with various pre-trained LLMs, including InternLM 2.5 and Qwen 2.5, using a randomly initialized MLP projector. As in the previous version, we applied a pixel unshuffle operation, reducing the number of visual tokens to one-quarter of the original. Besides, we adopted a similar dynamic resolution strategy as InternVL 1.5, dividing images into tiles of 448×448 pixels. The key difference, starting from InternVL 2.0, is that we additionally introduced support for multi-image and video data.