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AI モデル 2119

• chronos-bolt-base↗

  amazon

  🚀 **Update Feb 14, 2025**: Chronos-Bolt models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. Chronos-Bolt is a family of pretrained time series forecasting models which can be used for zero-shot forecasting. It is based on the T5 encoder-decoder architecture and has been trained on nearly 100 billion time series observations. It chunks the historical time series context into patches of multiple observations, which are then input into the encoder. The decoder then uses these representations to directly generate quantile forecasts across multiple future steps—a method known as direct multi-step forecasting. Chronos-Bolt models are up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. The following plot compares the inference time of Chronos-Bolt against the original Chronos models for forecasting 1024 time series with a context length of 512 observations and a prediction horizon of 64 steps.

• Ovis2.6-30B-A3B↗

  ATH-MaaS

  We introduce **Ovis2.6-30B-A3B**, the latest advancement in the Ovis series of Multimodal Large Language Models (MLLMs). Building on the strong foundation of Ovis2.5, Ovis2.6 upgrades the LLM backbone to a **Mixture-of-Experts (MoE)** architecture, delivering superior multimodal performance at a fraction of the serving cost. It also brings major improvements in long-context and high-resolution understanding, visual reasoning with active image analysis, and information-dense document comprehension. The following table presents a detailed performance comparison. Please note that superscripted results are sourced from external technical reports, and Qwen scores represent the highest value between its Think and Instruct versions. For quick reference, the best results are highlighted in red, and the second-best results are underlined. All values are rounded to one decimal place. The thinking and thinking budget logic can be applied in the same way for multi-image, video and pure text scenarios.

• GLM-5.3-Flash-abliterated-Maya-M-GGUF↗

  alesha-pro

  Project Maya's Maya-M quant of GLM-5.3-Flash plus a 2.2 MB vector that removes refusals while the model runs, and a small patch for the Maya engine that applies it. On 4x RTX 3090 it decodes 56 tokens/s at a 256K context with the vector on. The model weights in this repo are not mine. They are Maya-M and the vision files of Project Maya, copied byte for byte from peasantsmith/GLM-5.3-Flash-Maya-GGUF at revision `3e9f3bb20aa191e01c6fb850faf132d8149da98a`. The SHA256 of all 5 files match theirs at that revision. See `SHA256SUMS`. The authors changed the first shard after I downloaded it. Their commit `1084950a` renames the architecture in the GGUF header to `glm5-next`, and the file size is the same. This repo has the earlier first shard, the one all my runs used with Maya v1.0.27. For the current files go to their repo.

• Bonsai-Image-ternary-4B↗

  litert-community

  LiteRT is Google's on-device runtime, the new name for TensorFlow Lite (Android: `com.google.ai.edge.litert:litert`), and `litert-torch`, the renamed `ai-edge-torch`, is its PyTorch converter: a PyTorch model converted unmodified with `litert_torch.convert` matched the original to 4e-7 on a Galaxy S26 (measured, LiteRT 2.2.0, Android 16, 2026-09-05). PrismML Bonsai Image 4B — a ternary-weight diffusion transformer built on the FLUX.2-klein-4B architecture — converted to LiteRT for on-device text-to-image. The whole pipeline runs in three fixed-shape `.tflite` graphs; the host does only tokenization, the FlowMatch-Euler loop, and latent unpatchify (`generate.py` in this repo, ~150 lines, no torch and no diffusers). Smallest working set: **3.97 GiB** (int4 text encoder). Output is 512×512 with the default pair of graphs, or 256×256 with the `dit_256` / `vae_dec_256` pair (`generate.py --size 256`; `variants.256` in `pipeline_meta.json`) — the text encoder, tokenizer and latent constants are shared by both sizes; 256 prompt tokens and 4 sampling steps by default (the model is step-distilled; more steps also work).

• InternVL3-14B↗

  OpenGVLab

  We introduce InternVL3, an advanced multimodal large language model (MLLM) series that demonstrates superior overall performance. Compared to InternVL 2.5, InternVL3 exhibits superior multimodal perception and reasoning capabilities, while further extending its multimodal capabilities to encompass tool usage, GUI agents, industrial image analysis, 3D vision perception, and more. Additionally, we compare InternVL3 with Qwen2.5 Chat models, whose corresponding pre-trained base models are employed as the initialization of the langauge component in InternVL3. Benefitting from Native Multimodal Pre-Training, the InternVL3 series achieves even better overall text performance than the Qwen2.5 series. As shown in the following figure, InternVL3 retains the same model architecture as InternVL 2.5 and 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 3 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.

• DeepSeek-V4.1-Flash-UNCENSORED-DwarfStar-Q2↗

  pyrodog

  This is a community conversion of dealignAI's DeepSeek-V4.1-Flash-UNCENSORED-FP8 into the Q2 GGUF format that DwarfStar uses. **Why we're sharing it.** To save other people the trouble. We downloaded the source checkpoint, converted it on an Apple M5 Max MacBook Pro with 128 GB of unified memory, checked that the result worked, and hooked it up to OpenCode. If you want the same setup, you can download this file instead of pulling the roughly 510 GB source and spending several hours converting it yourself. This is an independent community effort, not an official release from DeepSeek, dealignAI, or DwarfStar. - **DeepSeek AI** built the original DeepSeek-V4.1-Flash model and its architecture. - **dealignAI** produced the abliterated checkpoint. Their model card credits **@dealignai and Jordan Schenck / @jordanschenck**. The abliteration is entirely their work. We didn't do it and don't take credit for it. - **Salvatore Sanfilippo (antirez) and the DwarfStar contributors** wrote the inference engine, the V4.1 converter, the quantization code, and the validation tools we used. DwarfStar in turn builds on **llama.cpp and the GGML authors**, whose copyright notices are kept in `LICENSE-DwarfStar`. - **pyrodog** ran the conversion, packaged it, did the local testing described below, and uploaded it, with help from OpenAI Codex. No training, fine-tuning, merging, or additional abliteration was done. The language weights are quantized from the source checkpoint; the optional vision tensors are copied unchanged in BF16.

• bert-base-uncased_clinical-ner↗

  samrawal

• ThinkingCap-Qwen3.8-27B-GGUF-RCO-4.2bpw↗

  bottlecapai

  A **14.3 GB** GGUF of bottlecapai/ThinkingCap-Qwen3.8-27B that runs on an **18 GB GPU with image input, MTP speculative decoding and a 32k-token context all on the card**, and matched the bf16 weights on all five benchmarks we evaluated (2,863 questions: 81.00% against 81.03%). The standard GGUFs (ThinkingCap-Qwen3.8-27B-GGUF) start at IQ4_XS, 15.5 GB, which with the vision projector and a 32k context does not fit 18 GB. Every weight tensor was quantized with llama.cpp's own types (IQ2_S up to Q8_0, all with one importance matrix computed on the model's own reasoning traces). Which type each of the 401 language-model weight matrices gets was then picked by an RCO search: it minimizes the KL divergence between the bf16 model's and the mixed model's next-token distributions on 256 of the model's own traces, under an exact file-size budget. The same idea as ISTA-DASLab's GSQ-RCO GGUFs, searched on this finetune rather than the base model; the rounding itself is llama.cpp's.

• parakeet-ultra-gguf↗

  handy-computer

  GGUF conversions of moondream/parakeet-ultra for use with transcribe.cpp. Ported from upstream commit 73175eb, pinned 2026-10-04. Validated against the kestrel 0.9.1 (Photon) reference at transcribe.cpp commit 08f9e6f0 on 2026-10-05. Moondream's post-trained parakeet-tdt-0.6b-v3. Offline multilingual speech-to-text over the same 25 European languages, with lower error rates than v3 on English and on every FLEURS language we measured. A FastConformer encoder with a TDT transducer decoder, plus a small voice-activity head that the model uses to cut long recordings at pauses into segments of at most 30 seconds. Takes 16 kHz mono WAV and produces a punctuated, cased transcript with optional token-level timestamps. A drop-in replacement for v3: same size, languages and API. Not a streaming model and does not translate.

• efficientnet_b1.ft_in1k↗

  timm

  A EfficientNet image classification model. Fine-tuned on ImageNet-1k from original Tensorflow "SAME" padding weights for use in PyTorch.