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AI モデル 2119

  • pythia-410m-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.**

  • BioLORD-2023↗

    FremyCompany

    This model was trained using BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts. State-of-the-art methodologies operate by maximizing the similarity in representation of names referring to the same concept, and preventing collapse through contrastive learning. However, because biomedical names are not always self-explanatory, it sometimes results in non-semantic representations. BioLORD overcomes this issue by grounding its concept representations using definitions, as well as short descriptions derived from a multi-relational knowledge graph consisting of biomedical ontologies. Thanks to this grounding, our model produces more semantic concept representations that match more closely the hierarchical structure of ontologies. BioLORD-2023 establishes a new state of the art for text similarity on both clinical sentences (MedSTS) and biomedical concepts (EHR-Rel-B).

  • Hy-Memory-30B-A3B↗

    腾讯

    **Hy-Memory-30B-A3B** is a compact long-term-memory writer model (~3B active parameters) developed by Tencent Hunyuan to execute the write path inside external memory frameworks. It directly replaces the general-purpose LLM inside **Mem0**, **MemOS**, and **Hy-Memory** (covering Chat, Working, and Coding scenarios), deciding what information to retain, how precisely to represent it, and how new evidence should update the persistent memory bank. The model is trained in two stages—Memory On-Policy Distillation (OPD) followed by Attributed Memory Reinforcement Learning—and transfers directly to unseen memory frameworks such as **EverMemOS**. Cross-harness macro-average scores on the fixed 20% benchmark matrix. Each harness score averages six metrics equally; Overall averages the four harnesses equally. EverMemOS is held out from training. Figure 1: Hy-Memory-30B-A3B is trained inside the same deployed harness configurations in both stages. OPD supplies dense teacher supervision on student-written state chains; RL examines the resulting banks, assigns chunk-level credit, and carries the selected bank across windows. In the rollout grid, each cell is the bank after chunk t of rollout i, shaded by its chunk score s_i[t]. Advantages are normalized within each column (dashed: one chunk-level group), so the best rollout can differ from chunk to chunk. The outlined cell is the final bank with the highest sum_t s_i[t], which becomes the parent of the next window.

  • Samantha-Qwen2-7B-GGUF↗

    mradermacher

  • gemma-4-31B-it-scotoma-2-GGUF↗

    ReadyArt

    --bg:#07090d; --panel:#0b1016; --ink:#edf1f4; --soft:#b0bac5; --dim:#7a8591; --faint:#59636f; --line:rgba(150,178,205,.14); --water:#6fc3d8; --water2:#4e95b5; --ember:#e2a24d; background:var(--bg); color:var(--ink); max-width:940px; margin:0 auto; font-family:system-ui,-apple-system,"Segoe UI",Roboto,Helvetica,Arial,sans-serif; font-size:16px; line-height:1.62; border:1px solid var(--line); overflow:hidden; } font-size:clamp(2.6rem,8.5vw,5.2rem); line-height:.92; text-transform:uppercase;} @supports not (-webkit-text-stroke:1px #fff){#scotoma2 .num2{color:var(--water);}} color:var(--soft); font-weight:400;} /* chart strip */ border-bottom:1px solid var(--line); background:var(--panel);} /* body */ font-weight:700; color:var(--ink);} font-family:ui-monospace,Menlo,monospace; font-size:.78rem; color:var(--water); letter-spacing:.05em; border:1px solid var(--line); padding:.15em .5em; border-radius:2px;} /* signs table */ color:var(--faint); font-weight:600; text-align:left; padding:8px 10px; border-bottom:1px solid var(--line);} font-family:ui-monospace,Menlo,monospace;} vertical-align:top;} white-space:nowrap; text-align:right;} /* presentation */ border-radius:2px; border-bottom:1px solid rgba(226,162,77,.55);} letter-spacing:.16em; text-transform:uppercase; color:var(--faint); margin-bottom:.7em;} padding:14px 18px 15px; margin:.55em 0; font-size:.92rem; line-height:1.68; color:var(--soft);} letter-spacing:.20em; text-transform:uppercase; color:var(--faint); margin-bottom:.55em;} border-radius:2px; border-bottom:1px solid rgba(226,162,77,.55); cursor:help;} font-size:.72rem; letter-spacing:.03em; display:flex; justify-content:space-between; gap:16px; flex-wrap:wrap;} @media (max-width:560px){#scotoma2 .chart{grid-template-columns:repeat(2,1fr);} #scotoma2 .cell:nth-child(2n){border-right:none;} #scotoma2 .masthead,#scotoma2 .body,#scotoma2 .foot{padding-left:22px;padding-right:22px;}} Base gemma-4-31B-it Method γ-fold + DPO Rounds 3 · 9.3k pairs Format thinking (channel) License Apache-2.0 Findings / what it does scotoma-2 is the successor to scotoma . It starts from the same idea, a bounded edit that loosens gemma‑4‑31B‑it’

  • MARBERT↗

    UBC-NLP

    **MARBERT** is one of three models described in our **ACL 2021 paper** **"ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic"**. MARBERT is a large-scale pre-trained masked language model focused on both Dialectal Arabic (DA) and MSA. Arabic has multiple varieties. To train MARBERT, we randomly sample 1B Arabic tweets from a large in-house dataset of about 6B tweets. We only include tweets with at least 3 Arabic words, based on character string matching, regardless whether the tweet has non-Arabic string or not. That is, we do not remove non-Arabic so long as the tweet meets the 3 Arabic word criterion. The dataset makes up **128GB of text** (**15.6B tokens**). We use the same network architecture as ARBERT (BERT-base), but without the next sentence prediction (NSP) objective since tweets are short. See our repo for modifying BERT code to remove NSP. For more information about MARBERT, please visit our own GitHub repo. If you use our models (ARBERT, MARBERT, or MARBERTv2) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated): We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, ComputeCanada and UBC ARC-Sockeye. We also thank the Google TensorFlow Research Cloud (TFRC) program for providing us with free TPU access.

  • Qwen3.8-Flash-Next-AP-GGUF↗

    agentionai

    > **Updated 2026-10-13: `AP-Q5_K_M`.** Same file name, size and tensor types, better weights from an improved encode: > KL divergence **0.0771** and top-1 agreement **86.95 %** on held-out text, **0.0181** on code: > level with unsloth's UD-Q4_K_XL on held-out text and ahead on code, in the same VRAM. Download the file again. > > **Updated 2026-10-12: `AP-Q4_K_XL`.** KL divergence **0.0886** and top-1 agreement **86.14 %** on held-out text, > **0.0206** on code. 0 loops in a 45-run loop stress test. Quantized for accuracy per gigabyte of VRAM, and measured — every tier below is scored against the same reference on a held-out corpus. Method and full results . Each tier varies the quant type per tensor group rather than using one type throughout, to get the most accuracy per gigabyte of VRAM.

  • neural-chat-7b-v3-3↗

    Intel

    This model is a fine-tuned 7B parameter LLM on the Intel Gaudi 2 processor from the Intel/neural-chat-7b-v3-1 on the meta-math/MetaMathQA dataset. The model was aligned using the Direct Performance Optimization (DPO) method with Intel/orca_dpo_pairs. The Intel/neural-chat-7b-v3-1 was originally fine-tuned from mistralai/Mistral-7B-v-0.1. For more information, refer to the blog The Practice of Supervised Fine-tuning and Direct Preference Optimization on Intel Gaudi2. Context length for this model: 8192 tokens (same as https://huggingface.co/mistralai/Mistral-7B-v0.1) Here is the sample code to reproduce the model: GitHub sample code. Here is the documentation to reproduce building the model:

  • distilhubert↗

    ntu-spml

    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. Paper: DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT

  • SageV2-8B-LongContext-NSFW-RP↗

    sinhal

    By turn thirty they're recycling the same sentence, every character sounds like the same actor in a different hat, and the one you wrote as a shy librarian is somehow flirting in the exact cadence of the last character you used. While testing samplers I ran a 40-turn conversation with a clingy-girlfriend character. By turn 30 she had **grown ears, a tail, and paws** — none of which appear anywhere in her character card. The model had drifted into a different trope entirely and then locked onto it. That's the failure this targets. Not "can it write one good paragraph" — whether the character you defined is still that character forty messages later.