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AI 模型 2119

• SmolLM-135M-GGUF↗

  QuantFactory

  library_name: transformers license: apache-2.0 language: - en datasets: - HuggingFaceTB/smollm-corpus 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..

• Cyber-Tiel-Coder-35B-A3B-MTP-TTQ-GGUF↗

  TokenTinkerer

  **TTQ** (TokenTinkerer Quant) is a ten-rung GGUF ladder built from peculiar-ragdoll/Cyber-Tiel-Coder-35B-A3B-GGUF-MTP, from 3.34 bpw to 6.64 bpw. Every rung is **tensor-for-tensor the same shape** as our published Qwen3.6-35B-A3B TTQ ladder — the same ten ftypes in the same places on 733 body tensors — with the model's grafted MTP head carried at the precision upstream ships it. Every claim below is a measurement, not an estimate. > ⚠️ **This is a quant of an abliterated (uncensored) model.** It inherits CyberTiel's behaviour and > its usage warnings: it will not refuse, and with tool access it can do real harm. Read > the base model card > before running it, and sandbox it at the OS level. > **Source caveat — there is no full-precision release of this model.** The ladder is built from > upstream's highest rung, `UD-Q8_K_XL` (~8.6 bpw), which is therefore **both** the requantization > source for every file here **and** the reference against which all KLD figures are measured. Two > consequences: every quality number is relative to *that file* rather than to true full precision, > and an unknown fraction of each divergence is inherited from it. Comparisons *between* files remain > valid — they share one reference — and the same caveat applies to our Qwen TTQ ladder.

• mms-tts-tur↗

  facebook

  This model is part of Facebook's Massively Multilingual Speech project, aiming to provide speech technology across a diverse range of languages. You can find more details about the supported languages and their ISO 639-3 codes in the MMS Language Coverage Overview, and see all MMS-TTS checkpoints on the Hugging Face Hub: facebook/mms-tts. VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior. A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers, much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to synthesise speech with different rhythms from the same input text.

• Ministral-3b-instruct↗

  ministral

  Ministral is a series of language model, build with same architecture as the famous Mistral model, but with less size. - **Model type:** A 3B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - **Language(s) (NLP):** Primarily English - **License:** Apache 2.0 - **Finetuned from model:** mistralai/Mistral-7B-v0.1

• gemma-4-E4B-it-qat-q4_0-gguf↗

  Google

  Hugging Face | GitHub | Launch Blog | Documentation | Technical Report > [!Note] > This model card is for the new versions of the Gemma 4 family optimized with Quantization-Aware Training (QAT), which allows preserving similar quality to bfloat16 while dramatically reducing the memory requirements to load the model. > Four versions of the QAT checkpoints are available: > * **Unquantized QAT checkpoints** (Q4_0): Half-precision weights extracted from the QAT pipeline, ideal for custom downstream compilation and research. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B, and their drafter models. > * **GGUF** (Q4_0): Ready-to-deploy formats for broad ecosystem compatibility. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B. > * **Mobile-optimized** (wNa8o8): A custom schema engineered explicitly for mobile hardware efficiency. It features targeted 2-bit decoding layers, optimized KV caches, and static activations to maximize VRAM savings. Available for Gemma 4 E2B and E4B. > * **Compressed Tensors** (w4a16): QAT checkpoints serialized in the compressed-tensors format for native, optimized inference with vLLM. Available for Gemma 4 E2B, E4B, 12B, and 31B. > * **Assistant Compatibility**: When using multi-token prediction (speculative decoding) with an assistant model alongside a QAT target model, the assistant model must also be a QAT checkpoint with the same precision to ensure compatibility. Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages.

• sentiment-analysis↗

  samadpls

• Depth-Anything-V2-Metric-Outdoor-Base-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:

• Arabic-handwritten-OCR-4bit-Qwen2.5-VL-3B-v2↗

  sherif1313

  🥇 The first open-source Arabic model to achieve 97.2% accuracy in extracting Arabic text from historical books and manuscripts. This model is the highest-performing open-source Arabic model ever for handwriting and manuscripts, trained on 65,747 diverse samples, including: Scale Value Explanation Evaluation Loss 0.6564, the lowest globally in Arabic OCR Character Error Rate (CER) 4.51%, excellent (less than 5% = high quality) Word Error Rate (WER) ~9%, very good for handwritten texts Estimated Accuracy 97.2%, outperforms commercial models Average Time 0.30-second inference, fast for live applications

• DeepSeek-V4-Flash-DSpark↗

  DeepSeek

  **Note: DeepSeek-V4-Flash-DSpark** is **not** a new model. It is the same checkpoint with an additional speculative decoding module attached. A minimal inference example is available in the `inference` folder. For more details, refer to: https://github.com/deepseek-ai/DeepSpec We present a preview version of **DeepSeek-V4** series, including two strong Mixture-of-Experts (MoE) language models — **DeepSeek-V4-Pro** with 1.6T parameters (49B activated) and **DeepSeek-V4-Flash** with 284B parameters (13B activated) — both supporting a context length of **one million tokens**. DeepSeek-V4 series incorporate several key upgrades in architecture and optimization:

• rinalmo-micro↗

  multimolecule

  This is an UNOFFICIAL implementation of the RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks by Rafael Josip Penić, et al. > [!TIP] > The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation. **The team releasing RiNALMo did not write this model card for this model so this model card has been written by the MultiMolecule team.**