전체 검색

AI 모델 2119

• pythia-1b-deduped↗

  EleutherAI

  The *Pythia Scaling Suite* is a collection of models developed to facilitate interpretability research (see paper). It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. We also provide 154 intermediate checkpoints per model, hosted on Hugging Face as branches. The Pythia model suite was designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models match or exceed the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. Previously, we released an early version of the Pythia suite to the public. However, we decided to retrain the model suite to address a few hyperparameter discrepancies. This model card lists the changes ; see appendix B in the Pythia paper for further discussion. We found no difference in benchmark performance between the two Pythia versions. The old models are still available, but we suggest the retrained suite if you are just starting to use Pythia. **This is the current release.**

• SmolLM-135M↗

  HuggingFaceTB

  1. Model Summary 2. Limitations 3. Training 4. License 5. Citation SmolLM is a series of state-of-the-art small language models available in three sizes: 135M, 360M, and 1.7B parameters. These models are built on Cosmo-Corpus, a meticulously curated high-quality training dataset. Cosmo-Corpus includes Cosmopedia v2 (28B tokens of synthetic textbooks and stories generated by Mixtral), Python-Edu (4B tokens of educational Python samples from The Stack), and FineWeb-Edu (220B tokens of deduplicated educational web samples from FineWeb). SmolLM models have shown promising results when compared to other models in their size categories across various benchmarks testing common sense reasoning and world knowledge. For detailed information on training, benchmarks and performance, please refer to our full blog post. While SmolLM models have been trained on a diverse dataset including educational content and synthetic texts, they have limitations. The models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content. For a more comprehensive discussion of the models' capabilities and limitations, please refer to our full blog post..

• HY-MT1.5-1.8B-GGUF↗

  腾讯

  🤗  Hugging Face   |   🕹️  Demo      🤖  ModelScope   |   Hunyuan Translation Model Version 1.5 includes a 1.8B translation model, HY-MT1.5-1.8B, and a 7B translation model, HY-MT1.5-7B. Both models focus on supporting mutual translation across 33 languages and incorporating 5 ethnic and dialect variations. Among them, HY-MT1.5-7B is an upgraded version of our WMT25 championship model, optimized for explanatory translation and mixed-language scenarios, with newly added support for terminology intervention, contextual translation, and formatted translation. Despite having less than one-third the parameters of HY-MT1.5-7B, HY-MT1.5-1.8B delivers translation performance comparable to its larger counterpart, achieving both high speed and high quality. After quantization, the 1.8B model can be deployed on edge devices and support real-time translation scenarios, making it widely applicable. - HY-MT1.5-1.8B achieves the industry-leading performance among models of the same size, surpassing most commercial translation APIs. - HY-MT1.5-1.8B supports deployment on edge devices and real-time translation scenarios, offering broad applicability. - HY-MT1.5-7B, compared to its September open-source version, has been optimized for annotated and mixed-language scenarios. - Both models support terminology intervention, contextual translation, and formatted translation.

• gemma-3-12b-it-qat-q4_0-unquantized↗

  Lightricks

  > [!Note] > This repository corresponds to the 12B **instruction-tuned** version of the Gemma 3 model using Quantization Aware Training (QAT). > > **The checkpoint in this repository is unquantized, please make sure to quantize with Q4_0 with your favorite tool** > > Thanks to QAT, the model is able to preserve similar quality as `bfloat16` while significantly reducing the memory requirements > to load the model. * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3] Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone.

• zerank-2-reranker-vllm↗

  polaria-tech

  **ZeroEntropy's zerank-2 reranker, ready for `vllm serve`.** Same weights, same scores, about twice as fast as the official path. Serving zerank-2 on vLLM is deceptively easy to get wrong: the server starts, every request returns 200, and the scores are quietly off. We converted the model, measured it against the official implementation, benchmarked it on an H100 and wrote down every trap we hit, so you can just serve it. > [!TIP] > **Also available in FP8:** `polaria-tech/zerank-2-reranker-vllm-fp8`. > Same results (NDCG@10 within ±0.004 on 4 MTEB reranking tasks), ~1.2x faster on H100 and half the > size (4.2 GB). Needs a GPU with native FP8 (Ada, Hopper or newer).

• Qwen3.8-27B-W4A16-AutoRound-GPTQ↗

  Vishva007

  This repository contains a **W4A16 (4-bit weights, 16-bit activations)** quantized version of Qwen/Qwen3.8-27B generated using Intel's AutoRound algorithm. The model was calibrated and quantized using production-grade settings to minimize accuracy degradation while significantly lowering VRAM requirements: * **Quantization Algorithm:** AutoRound * **Bits / Precision:** W4A16 (4-bit integer weights, 16-bit activation) * **Group Size:** 64 (provides higher reconstruction fidelity than standard 128) * **Symmetric (`sym`):** `True` * **Calibration Samples (`nsamples`):** 512 * **Tuning Iterations (`iters`):** 600 * **Vision Tower (`quant_nontext_module`):** `False` (Kept in **BF16** to preserve visual reasoning and OCR precision) * **Special Modules (`layer_config`):** Multi-Token Prediction (`mtp`, `mtp.fc`) layers preserved in native `bfloat16`.

• LFM3-Omni-2B-GGUF↗

  LiquidAI

  LFM3-Omni-2B is an omni-modal model that understands text, images, video and audio, and answers in text and speech. It is built for on-device use. Recommended sampling: greedy decoding (temperature 0) for text output. For speech output, audio temperature 0.4 and audio repetition penalty 1.2. > [!NOTE] > Supported in llama.cpp b11960 (v0.8.0) and newer, including real-time voice conversations (with tool calls) on the `/v1/realtime` WebSocket API of `llama serve`.

• Noct-Q-Uncensored-Qwen-Image-2.1↗

  Noctaluna

  Qwen Image 2.1, uncensored: nudes and explicit scenes render without a LoRA. Built with Qwen. **The full gallery with prompts, the int4, fp8, bf16 and fp16 files and the reviews are on Civitai: Noct Q.** - Full-body nudes in daylight, flash and night scenes - Explicit scenes with a partner: V4 renders them as asked three times as often as V3 - Readable text on signs and labels - Anime and painted illustration - Plain-caption prompts: who, what she does, where, the light - Image editing with the same files, in ComfyUI's Qwen Image 2.1 edit template

• bigvgan_v2_22khz_80band_256x↗

  NVIDIA

  - **Jul 2024 (v2.3):** - General refactor and code improvements for improved readability. - Fully fused CUDA kernel of anti-alised activation (upsampling + activation + downsampling) with inference speed benchmark. - **Jul 2024 (v2.2):** The repository now includes an interactive local demo using gradio. - **Jul 2024 (v2.1):** BigVGAN is now integrated with 🤗 Hugging Face Hub with easy access to inference using pretrained checkpoints. We also provide an interactive demo on Hugging Face Spaces.

• LTX2.3-10Eros↗

  TenStrip

  v1.5 Changelog: 1.4 and DMD only struggled with one thing, explicit motion and anatomy that was easy to access in version 1.2 and sulphur. All that data is still inside the model; versions 1.2, 1.3, 1.4, and 1.5 are almost all the same model. The difference is how the attn_2 and attn_1 gates are handled. 1.5 is 1.4 with a linear ramp across the gates to open up those explicit details from sulphur like they are on version 1.2's blocks. It still mostly maintains 1.4's base model style prompting and more stable outputs and structure. Because it is a hybrid of a model made for DMD and a model made for LTX distilled lora use, it goes best with a hybrid lora to make use of the motion vareity. Currently the hybrid block DMD lora here seems to work nicely: https://huggingface.co/TenStrip/LTX2.3_DMD_Lora/blob/main/LTX2.3_DMD_hybrid_v2.safetensors or these ones: https://huggingface.co/maximsobolev275/LTX2.3-DMD-Distill-Lora-Hybrids 1.5 INT8 with hybrid v2 merge: https://huggingface.co/CornLogic/10EROS-INT8/tree/main 1.5 INT8 quality version: https://huggingface.co/LokkenJP/10EROS_1.5_fp8_exp_learned