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AI モデル 2119
- speechless-code-mistral-7b-v1.0↗
uukuguy
Use the following dataset to fine-tune mistralai/Mistral-7B-v0.1 in order to improve the model's reasoning and planning abilities. Total 201,981 samples. - jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 23,462 samples. - Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 74,440 samples. - garage-bAInd/Open-Platypus: 100%, 24,926 samples. - WizardLM/WizardLM_evol_instruct_V2_196k: Coding coversation part. 30,185 samples - TokenBender/python_eval_instruct_51k: “python” in output .40,309 samples - Spider: 8,659 samples
- privacy-filter-nemotron-v2↗
OpenMed
`OpenMed/privacy-filter-nemotron-v2` is the second-generation Nemotron-schema checkpoint in the OpenMed privacy-filter family. It keeps the same fine-grained 55-category PII vocabulary as `OpenMed/privacy-filter-nemotron`, while using a broader training mix and a more recall-oriented adaptation recipe. In practice, this v2 checkpoint should perform better as a general PII masking and redaction model while preserving the useful typed labels from the original Nemotron model. The model is based on `openai/privacy-filter`, a 1.4B-parameter MoE token classifier with roughly 50M active parameters per token. It predicts 221 BIOES token classes: Use this checkpoint when you want the Nemotron fine-grained label schema, but prefer the improved v2 masking behavior.
- Qwen-Image-2.1-Turbo-mflux-6bit↗
OsaurusAI
An Osaurus 6-bit mflux quantization of Qwen/Qwen-Image-2.1-Turbo for Apple Silicon (14 GB, balanced size and quality). This is not an official Qwen release and is not endorsed by the Qwen team. Turbo is the 8-step distilled checkpoint of Qwen-Image-2.1. One bundle does both **text-to-image** and **reference editing** (up to 10 ordered reference images); the vision encoder is included. Requires **Osaurus 0.25.21 or newer**. Older versions treat this bundle as Qwen-Image-2.1 and sample it on that model's 40-step schedule, which gives degraded images.
- vad-crdnn-libriparty↗
speechbrain
This repository provides all the necessary tools to perform voice activity detection with SpeechBrain using a model pretrained on Libriparty. The pre-trained system can process short and long speech recordings and outputs the segments where speech activity is detected. The output of the system looks like this: The system expects input recordings sampled at 16kHz (single channel). If your signal has a different sample rate, resample it (e.g., using torchaudio or sox) before using the interface.
- Falcon3-1B-Instruct↗
tiiuae
**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B parameters. This repository contains the **Falcon3-1B-Instruct**. It achieves strong results on reasoning, language understanding, instruction following, code and mathematics tasks. Falcon3-1B-Instruct supports 4 languages (English, French, Spanish, Portuguese) and a context length of up to 8K. We report in the following table our internal pipeline benchmarks. - We use lm-evaluation harness. - We report **raw scores** obtained by applying chat template and fewshot_as_multiturn. - We use same batch-size across all models.
- test-gguf-moe-sample↗
SzymonOzog
- wide_resnet101_2.tv_in1k↗
timm
This model features: * ReLU activations * single layer 7x7 convolution with pooling * 1x1 convolution shortcut downsample - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 126.9 - GMACs: 22.8 - Activations (M): 21.2 - Image size: 224 x 224 - **Papers:** - Wide Residual Networks: https://arxiv.org/abs/1605.07146 - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 - **Original:** https://github.com/pytorch/vision Explore the dataset and runtime metrics of this model in timm model results.
- Yi-1.5-34B↗
01-ai
Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. Model | Context Length | Pre-trained Tokens | :------------: | :------------: | :------------: | | Yi-1.5 | 4K, 16K, 32K | 3.6T
- BgGPT-Gemma-3-27B-IT-GGUF↗
INSAIT-Institute
GGUF quantized versions of BgGPT-Gemma-3-27B-IT for use with llama.cpp, Ollama, LM Studio, and other GGUF-compatible tools. BgGPT 3.0 is a series of Bulgarian-adapted LLMs based on Gemma 3, developed by INSAIT. 1. **Vision-language understanding** — The models understand both text and images within the same context. 2. **Instruction-following** — Trained on a broader range of tasks, multi-turn conversations, complex instructions, and system prompts. 3. **Longer context** — Effective context of 131k tokens for longer conversations and complex instructions. 4. **Updated knowledge cut-off** — Pretraining data up to May 2025, instruction fine-tuning up to October 2025. Search for `BgGPT-Gemma-3-27B-IT-GGUF` in the model browser, or download a GGUF file manually and load it.
- Lemur-70B-Chat-v1-GGUF↗
TheBloke
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible. The creator of the source model has listed its license as `cc-by-nc-4.0`, and this quantization has therefore used that same license. As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.