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AI 模型 2119
- MiniCPM5-2B-Midtrain↗
openbmb
We are releasing **MiniCPM5-2B**, the second model in the **MiniCPM5** series, following MiniCPM5-1B. It is a dense 2B Transformer that scales up the same training recipe, built for on-device, local deployment, and resource-constrained scenarios, reaching 2B-class open-source SOTA. 🏆 **2B-class open-source SOTA**: compared with strong open-source models of similar size, MiniCPM5-2B achieves SOTA performance within this comparison set. It remains competitive with 4B-class models overall, while showing its advantages over models of comparable size in coding, mathematics, long-context understanding, tool use, and agentic tasks. #capability-comparison-radar { --foreground: #171717; display: block; width: 100%; max-width: 620px; margin: 0 auto; background: #fff; font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "PingFang SC", "Hiragino Sans GB", "Microsoft YaHei", sans-serif; } #capability-comparison-radar svg { display: block; width: 100%; height: auto; background: #fff; } #capability-comparison-radar .title { fill: var(--foreground); font-size: 16px; font-weight: 600; letter-spacing: 0; } #capability-comparison-radar .axis-label { fill: #333; font-size: 12px; font-weight: 600; } #capability-comparison-radar .ring { fill: none; stroke: rgba(128,128,128,.18); stroke-width: .8; } #capability-comparison-radar .spoke { stroke: rgba(128,128,128,.25); stroke-width: .8; } #capability-comparison-radar .ring-label { fill: #8a8a8a; font-size: 9px; } #capability-comparison-radar .series { stroke-linejoin: round; } #capability-comparison-radar .series.primary { stroke-width: 2.4; } #capability-comparison-radar .legend-label { fill: #171717; font-size: 12px; font-weight: 600; } #capability-comparison-radar .legend-average { fill: #666; font-size: 11px; } #capability-comparison-radar .legend-swatch { rx: 3; } @media (max-width: 900px) { #capability-comparison-radar { overflow-x: auto; } #capability-comparison-radar svg { min-width: 620px; } }
- Voxtral-Mini-3B-2507-gguf↗
handy-computer
GGUF conversions of mistralai/Voxtral-Mini-3B-2507 for use with transcribe.cpp. Ported from upstream commit 3060fe3, pinned 2026-06-06. Validated against the Transformers reference at transcribe.cpp commit 483c122 on 2026-06-06. Offline audio-LLM speech-to-text and speech translation. A Whisper-large-v3 bidirectional audio encoder feeds a 4-frame-group projector (375 audio tokens per 30 s chunk) into a Ministral-3B causal LM (30 layers, GQA 32/8, NEOX RoPE, SwiGLU) via audio-token injection. Takes a 16 kHz mono WAV and produces a transcript via greedy decoding; speech translation runs through the mistral-common instruct template. The smaller sibling of Voxtral Small 24B — same encoder, projector, log-mel frontend, and tekken tokenizer, with a 3B decoder in place of Mistral-Small-24B.
- tmax-9b-gguf↗
samuelchristlie
- Depth-Anything-V2-Metric-Outdoor-Large-hf↗
depth-anything
This model represents a fine-tuned version of Depth Anything V2 for outdoor metric depth estimation using the synthetic Virtual KITTI datasets. Depth Anything V2 was introduced in the paper of the same name by Lihe Yang et al. It uses the same architecture as the original Depth Anything release but employs synthetic data and a larger capacity teacher model to achieve much finer and robust depth predictions. This fine-tuned version for metric depth estimation was first released in this repository. **Six metric depth models** of three scales for indoor and outdoor scenes, respectively, were released and are available:
- gemma-3-27b-it-int4-awq↗
gaunernst
This is the QAT INT4 Flax checkpoint (from Kaggle) converted to HF+AWQ format for ease of use. AWQ was NOT used for quantization. You can find the conversion script `convert_flax.py` in this model repo. NOTE: this is NOT the same as the official QAT INT4 GGUFs released here https://huggingface.co/collections/google/gemma-3-qat-67ee61ccacbf2be4195c265b Below is the original Model card from https://huggingface.co/google/gemma-3-27b-it
- mitra-regressor↗
autogluon
Mitra regressor is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random regressors. Mitra is based on a 12-layer Transformer of 72 M parameters, pre-trained by incorporating an in-context learning paradigm. Amazon Science blog: Mitra: Mixed synthetic priors for enhancing tabular foundation models
- xlm-v-base↗
facebook
XLM-V is multilingual language model with a one million token vocabulary trained on 2.5TB of data from Common Crawl (same as XLM-R). It was introduced in the XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models paper by Davis Liang, Hila Gonen, Yuning Mao, Rui Hou, Naman Goyal, Marjan Ghazvininejad, Luke Zettlemoyer and Madian Khabsa. **Disclaimer**: The team releasing XLM-V did not write a model card for this model so this model card has been written by the Hugging Face team. This repository documents all necessary integeration steps. > Large multilingual language models typically rely on a single vocabulary shared across 100+ languages. > As these models have increased in parameter count and depth, vocabulary size has remained largely unchanged. > This vocabulary bottleneck limits the representational capabilities of multilingual models like XLM-R. > In this paper, we introduce a new approach for scaling to very large multilingual vocabularies by > de-emphasizing token sharing between languages with little lexical overlap and assigning vocabulary capacity > to achieve sufficient coverage for each individual language. Tokenizations using our vocabulary are typically > more semantically meaningful and shorter compared to XLM-R. Leveraging this improved vocabulary, we train XLM-V, > a multilingual language model with a one million token vocabulary. XLM-V outperforms XLM-R on every task we > tested on ranging from natural language inference (XNLI), question answering (MLQA, XQuAD, TyDiQA), and > named entity recognition (WikiAnn) to low-resource tasks (Americas NLI, MasakhaNER).
- granite-timeseries-flowstate-r1↗
ibm-granite
**Important**: To use FlowState-r1.1 include the `revision=r1.1` argument when loading the model, as demonstrated in the `Recommended Use` section. If no `revision` argument is provided, the version 1.0 is used. - **FlowState**: We present an SSM-based time series foundation model that can be dynamically adjusted to the specific characteristics of the time series during evaluation. - **Functional Basis Decoder (FBD)**: We propose a novel decoder, as a critical component of FlowState, that utilizes a set of continuous basis functions to make continuous forecasts and allow seamless adjustment to specific input characteristics. - **Flexible temporal adaptation**: FlowState can dynamically adjust the context and target length to the timescale of the provided time series. - **Compact and high-performing**: With fewer than 10M parameters and the ability to forecast multiple consecutive patches in parallel, FlowState delivers state-of-the-art accuracy with exceptional efficiency. This model card contains the model weights for the Granite version of the FlowState model, which is suitable for commercial and enterprise use. A research version of the model (available here) is also provided and is intended for research use only. The Granite and research versions achieve the same MASE performance on GIFT‑Eval, indicating comparable point forecasting accuracy. The research version demonstrates slightly improved CRPS performance, which evaluates the quality of probabilistic forecasts. Despite being **more than 10x smaller** than the next best models, FlowState is **better than all Zero-Shot models** with publicly available source code and model weights on the GIFT-Eval Leaderboard in terms of MASE performance, as of Apr. 28th 2026. The Figure compares GIFT MASE Performance vs. model size for FlowState and the most competitive Zero-Shot Models.
- Qwen3-4B-abliterated-GGUF↗
Mungert
Our latest quantization method introduces **precision-adaptive quantization** for ultra-low-bit models (1-2 bit), with benchmark-proven improvements on **Llama-3-8B**. This approach uses layer-specific strategies to preserve accuracy while maintaining extreme memory efficiency. All tests conducted on **Llama-3-8B-Instruct** using: - Standard perplexity evaluation pipeline - 2048-token context window - Same prompt set across all quantizations **Key**: - PPL = Perplexity (lower is better) - Δ PPL = Percentage change from standard to DynamicGate - Speed = Inference time (CPU avx2, 2048 token context) - Size differences reflect mixed quantization overhead
- InternVL3-8B↗
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.