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AI 模型 2119

• Ingot-XII-27B-Q2↗

  dendriteholdings

  A public mirror of a model that won on Ingot, a Bittensor subnet where miners quantize a fixed teacher model and the validator crowns the copy that stays closest to it. It is Prism ML's Ternary Bonsai 2 27B, refined: the same ternary codes, with the per-block scales and the norm weights re-tuned by the miner to bring it closer to Qwen3.8-27B. The weights are the miner's upload, byte for byte (`Ingot-XII-27B-PQ2_0.gguf`); `Ingot-XII-27B-PTQ1_0.gguf` repacks them losslessly at 1.75 bits per weight. Credit belongs to the miner, and to Prism ML for Bonsai 2.

• rizzo-flow↗

  rizzoaiacademy

  These are LoRA fine-tunes of XHToken/Spark-X2.5-4B (recommended) and XHToken/Spark-X2.5-1.7B, for Rizzo Flow. Rizzo Flow turns an unstructured state into typed decisions (`boolean`, `choice`, `score`) with probabilities, and it **generates no text**. The model runs one forward pass per question. The answer is the softmax over the logits of the answer letters (`A`, `B`, …) at the last prompt position. Training works the same way. There is no text target: the loss is a soft cross-entropy between the target distribution and the softmax over the answer letters. Only the LoRA adapters are trained. These GGUF files are what Rizzo Flow downloads by default. It pins this repository by commit and checks the sha256 of every file:

• samvit_large_patch16.sa1b↗

  timm

• sambanovasystems_-_SambaLingo-Arabic-Base-70B-gguf↗

  RichardErkhov

• Qwen3.8-27B-GGUF↗

  unsloth

  Introducing Dynamic V3.0 GGUFs for SOTA accuracy and quantization performance Run and fine-tune Qwen3.8 in Unsloth Desktop with Thinking toggles . Download for Mac, Windows and Linux. GitHub repo Developer Role Support so Qwen3.8 can work in agentic tools like Codex and more! Tool calling improvements: Makes parsing nested objects to make tool calling succeed more. See below for 4-bit Qwen3.8-27B run inside of Unsloth Desktop: Analysis of best Qwen3.8 GGUF providers. Unsloth Dynamic v3.0 delivers >10% top-1% better accuracy at the same size compared to every other provider. Read more Following the widespread community adoption of the Qwen3.5 and Qwen3.6 series, we are pleased to introduce Qwen3.8, the most capable generation in the Qwen open-model family to date.

• chronos-t5-base↗

  autogluon

  🚀 **Update Feb 14, 2025**: Chronos-Bolt & original Chronos 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. 🚀 **Update Nov 27, 2024**: We have released Chronos-Bolt⚡️ models that are more accurate (5% lower error), up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. Check out the new models here. Chronos is a family of **pretrained time series forecasting models** based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes.

• vocos-mel-24khz↗

  charactr

  Vocos is a fast neural vocoder designed to synthesize audio waveforms from acoustic features. Trained using a Generative Adversarial Network (GAN) objective, Vocos can generate waveforms in a single forward pass. Unlike other typical GAN-based vocoders, Vocos does not model audio samples in the time domain. Instead, it generates spectral coefficients, facilitating rapid audio reconstruction through inverse Fourier transform.

• Qwen3.8-Flash-Next-NVIDIA-NVFP4-Strata↗

  Maximilian228

  > **Quantized by NVIDIA.** These are the weights of NVIDIA's checkpoint > `nvidia/Qwen3.8-Flash-Next-NVFP4` (NVIDIA Model > Optimizer), unchanged. This repository only converts their file format for Strata NVFP4. It is not made or endorsed by > NVIDIA. - **Accuracy:** how often the model's most likely next token is the Q8_0 model's. Measured on 5,153 tokens of code, docs, English and Ukrainian chats and agent transcripts, every model fed the same text. Every model ran with its experts on the CPU and the route tail skip off, so only the quantization differs. The Q8_0 model also has Q8_0 attention, Qwen's BF16 router and Qwen's own FP8 PLE, so it is the original's precision everywhere but the 8-bit weights. - **Speed:** RTX 5090 (32 GB), Ryzen 9 9950X3D, 128 GB of DDR5-5600, Windows 11, Strata NVFP4 0.1.42-nvfp4.1's engine with the arguments 0.1.42-nvfp4.2's setup writes for each model. Median of 3 runs: 4 greedy chats (up to 1,500 tokens each) and one 32,007-token prompt. - **RAM:** "RAM in use" is the engine and the server at the speed shown; "PC for this speed" is the installed RAM that holds it with Windows beside it. Smaller PCs still run the models, slower: the NVFP4 GPTQ and NVIDIA's on a 64 GB PC in the low-RAM mode decode ~115 tokens/s and read a 32K prompt in ~9 s (docs/NVFP4.md, "64 GB of RAM"); Unsloth's files go into a RAM-budget mode there and read experts from the SSD (UD-Q4_K_XL: 7-8.5 tokens/s at 64 GB, upstream's measurement); setup's minimum is 48 GB for them and the ISTA 2-bit files, 60-64 GB for the ISTA 3-bit ones. - **Unsloth's files keep every expert in RAM where they fit with 16 GiB more** (setup from 0.1.42-nvfp4.2): from ~88 GB of RAM for UD-Q4_K_XL and ~68 GB for UD-Q3_K_XL. Below that, setup's RAM-budget mode reads the rest from the GGUF on the SSD. Setup before 0.1.42-nvfp4.2 used the budget mode on every PC; on 128 GB UD-Q4_K_XL then decoded 118 tokens/s and read the 32K prompt in 30 s (run setup again to switch an existing install). - UD-Q3_K_XL's experts are i-quants despite the name: IQ3_XXS gate/up with IQ4_NL or Q8_0 down. - The GGUF models run upstream's route tail skip by default: ~10% faster decode, and answers a little further from Q8_0 than the table shows. - The two 128 GB models need a 128 GB PC (setup asks below that). Q8_0 runs there in the low-RAM mode by itself (100 GiB of experts locked in RAM); the mix keeps eve

• tiny-random-deepseek-distill-qwen-gguf↗

  sammysun0711

• embeddinggemma-300m↗

  unsloth

  **Model Page**: EmbeddingGemma * Responsible Generative AI Toolkit * EmbeddingGemma on Kaggle * EmbeddingGemma on Vertex Model Garden EmbeddingGemma is a 300M parameter, state-of-the-art for its size, open embedding model from Google, built from Gemma 3 (with T5Gemma initialization) and the same research and technology used to create Gemini models. EmbeddingGemma produces vector representations of text, making it well-suited for search and retrieval tasks, including classification, clustering, and semantic similarity search. This model was trained with data in 100+ spoken languages.