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AI モデル 2119

• CodeBERTa-small-v1↗

  huggingface

  CodeBERTa is a RoBERTa-like model trained on the CodeSearchNet dataset from GitHub. The **tokenizer** is a Byte-level BPE tokenizer trained on the corpus using Hugging Face `tokenizers`. Because it is trained on a corpus of code (vs. natural language), it encodes the corpus efficiently (the sequences are between 33% to 50% shorter, compared to the same corpus tokenized by gpt2/roberta).

• SambaLingo-Russian-Chat-i1-GGUF↗

  mradermacher

• SAM-GGUF↗

  TheBloke

• pplx-decider-flash-gguf-v1.2↗

  perplexity-ai

  pplx-decider-flash-v1.2 is the small version of pplx-decider-v1.2: the same joint request format, the same API and the same text and image support, at about one seventh of the size. It is built on Qwen3.5-4B, **distilled from the 27B decision models** on their training requests (the student learns the teachers' full probability distributions, not just their answers). It combines several training runs through model souping (weight averaging). Use it when latency or cost matters more than the last points of accuracy. On our evaluations it keeps most of the 27B model's Decision Index and vision scores (see below). Distillation also improves results on decision tasks held out from all training. Stock `llama-server` does not implement this checkpoint's protocol v3; its `/v1/systemone` uses protocol v1. A compatible runtime must:

• wav2vec2-base↗

  facebook

  The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. **Abstract** We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.

• vakyansh-wav2vec2-punjabi-pam-10↗

  Harveenchadha

  Fine-tuned on Multilingual Pretrained Model CLSRIL-23. The original fairseq checkpoint is present here. When using this model, make sure that your speech input is sampled at 16kHz. **Note: The result from this model is without a language model so you may witness a higher WER in some cases.**

• internlm2-1_8b-reward↗

  internlm

  **InternLM2-1.8B-Reward** is a reward model trained on the foundation of InternLM2-Chat-1.8B-SFT. This model has been trained using over 2.4 million preference samples, both human-annotated and AI-synthesized, achieving outstanding performance while ensuring a balance between helpful and harmless. - **Variety of Sizes Available**: Our open-sourced reward models are available in sizes of **1.8B, 7B, and 20B**, each demonstrating exceptional performance across various metrics. We aim for these different-sized models to facilitate research on the scaling laws of reward models, providing valuable insights to the community. - **Comprehensive Coverage of Preference**: Trained with **2.4 million** preference pairs derived from both human annotations and AI synthesis, covering diverse areas such as dialogue, writing, poetry, summarization, coding, mathematics, etc. It also maintains a balance between helpful and harmless. - **Multilingual Support**: InternLM2-Reward was trained on high-quality **English and Chinese** preference data, delivering robust performance in both languages. This model was applied to the RLHF training process of InternLM2-Chat. The reward model training techniques from the InternLM2 Technical Report have been open-sourced in XTuner, try it out here!

• privacy-filter-multilingual↗

  OpenMed

  Fine-tuned `openai/privacy-filter` for **fine-grained PII extraction** across **54 categories** in **16 languages**. - **Base model**: `openai/privacy-filter` — 1.4B-parameter MoE (50M active per token), BIOES token-classification head - **Task**: Token classification for PII detection (BIOES scheme) - **Languages (16)**: Arabic, Bengali, Chinese, Dutch, English, French, German, Hindi, Italian, Japanese, Korean, Portuguese, Spanish, Telugu, Turkish, Vietnamese - **Training data**: Multilingual mix from AI4Privacy — `pii-masking-200k`, `pii-masking-400k`, and `open-pii-masking-500k-ai4privacy`, language-balanced - **Recipe**: `opf train` (OpenAI's official fine-tuning CLI) — full fine-tune, AdamW, balanced language sampling, 5 epochs, bf16 - **Labels**: 54 PII categories → 217 BIOES classes (1 `O` + 54 × B/I/E/S) The base model ships with 8 coarse PII categories and English-only training. This model trades that for a **6.75× more granular vocabulary** spanning identity, contact, address, financial, vehicle, digital, and crypto labels — all evaluated across 16 languages.

• colSmol-500M↗

  vidore

  ColSmolVLM is a model based on a novel model architecture and training strategy based on Vision Language Models (VLMs) to efficiently index documents from their visual features. It is a SmolVLM extension that generates ColBERT- style multi-vector representations of text and images. It was introduced in the paper ColPali: Efficient Document Retrieval with Vision Language Models and first released in this repository This version is trained with the commit b983e40 of the Colpali repository. (main branch from the repo) Our training dataset of 127,460 query-page pairs is comprised of train sets of openly available academic datasets (63%) and a synthetic dataset made up of pages from web-crawled PDF documents and augmented with VLM-generated (Claude-3 Sonnet) pseudo-questions (37%). Our training set is fully English by design, enabling us to study zero-shot generalization to non-English languages. We explicitly verify no multi-page PDF document is used both *ViDoRe* and in the train set to prevent evaluation contamination. A validation set is created with 2% of the samples to tune hyperparameters.

• pythia-6.9b-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.**