la_core_web_sm
latincy基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗DISCOVER & COMPARE
Explore AI models 85001
la_core_web_trf
latincy基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗latin-bert
latincy基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗turnsense
latishab基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Equinox-31B-GGUF
LatitudeGames基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Harbinger-24B-GGUF
LatitudeGames基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Hearthfire-24B-GGUF
LatitudeGames基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Muse-12B-GGUF
LatitudeGames基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Nova-70B-Llama-3.3-GGUF
LatitudeGames基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Wayfarer-2-12B-GGUF
LatitudeGames基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Wayfarer-Large-70B-Llama-3.3-GGUF
LatitudeGames基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗POLITICS
launch基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗ThinkPRM-1.5B
launchThinkPRM-1.5B is a generative Process Reward Model (PRM) based on the R1-Distill-Qwen-1.5B architecture. It is fine-tuned to perform step-by-step verification of reasoning processes (like mathematical solutions) by generating an explicit verification chain-of-thought (CoT) that involves labeling every step. It is designed to be highly data-efficient, requiring significantly less supervision data than traditional discriminative PRMs while achieving strong performance. ThinkPRM-1.5B provides step-level verification scores by generating natural language critiques and correctness judgments for each step in a given solution prefix. It leverages the underlying reasoning capabilities of the base Large Reasoning Model (LRM) and enhances them through fine-tuning on a small (1K examples) dataset of synthetically generated verification CoTs. These synthetic CoTs were produced by prompting QwQ-32B-Preview and filtered against ground-truth step labels from the PRM800K dataset to ensure quality. The model uses a standard language modeling objective, making it interpretable and allowing it to scale process verification compute by generating longer or multiple verification CoTs. It demonstrated superior performance compared to LLM-as-a-judge and discriminative PRM baselines (based on the same R1-Distill-Qwen-1.5B model but trained on ~100x more labels) on benchmarks including ProcessBench, MATH-500, AIME '24, GPQA-Diamond, and LiveCodeBench.
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View details ↗Qwen3.8-27B-PARO-int5
Launch80基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗OpenLID
laurievb基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗OpenLID-v2
laurievb基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗GLM-5.3-Flash-UD-Q4_K_XL-layers
lausannequants基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Nova-Qwen2.5-14B-Secure-Coder
Lautloserspieler基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Holo4-27B-Abliterix-GSQ-RCO-GGUF
lavendercomputing基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Holo4-35B-A3B-Heretic-GSQ-RCO-GGUF
lavendercomputing基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗LFM2.5-24B-A2B-Heretic-GSQ-RCO-GGUF
lavendercomputing基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Ornith-1.5-35B-A3B-Heretic-MTP-APEX-GSQ-RCO-GGUF
lavendercomputingOrnith-1.5-35B-A3B (vendor: `ornith-ai`) is a ~35B mixture-of-experts model with ~3B activated parameters per token (≈70 GB in bf16), 262,144-token context, built on top of Qwen3.5/Gemma4 by continued pretraining + reinforcement self-improvement. It is a *reasoning model*: its assistant turns open with a ` … ` chain-of-thought block, and it emits ` ` blocks for function calling. Vendor card: https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B The uncensored artifacts on HF were produced by the community with **Heretic** (https://github.com/p-e-w/heretic, v1.4.0 lineage) — the fully automated abliteration tool. Abliteration is a *training-free* modification: it estimates the "refusal direction" in the model's activation space and projects that direction out of selected weight matrices, disabling refusal behaviour without any fine-tuning, then validates against a behavioral test suite and keeps the best pass. Heretic therefore changes a small fraction of weights only; the vendor's reasoning/tool-calling behaviour and general capability are otherwise preserved as far as the trial's KL-bounded search allows. 1. **No Heretic run of our own.** Running Heretic on this 35B model locally (4× V100, CPU-only tooling) is a multi-day job with no guarantee of a good pass. Instead we reused the best public Heretic output: `SC117/Ornith-1.5-35B-A3B-Heretic-MTP-APEX-GGUF`, tier `I-Quality` — verified genuine Heretic 1.4.0 output (Trial 62), dequantised ≈ 5.29 bpw. The vendor's own BF16 repo (`ornith-ai/Ornith-1.5-35B-A3B-GGUF` / `ornith-ai/Ornith-1.5-35B-A3B`) is *not* abliterated, and no BF16 Heretic checkpoint was ever published, so every public uncensor of this model is one Heretic generation downstream of a full-precision master. 2. **The source is already quantized.** The abliterated master was never released at bf16; the best public artifact is a carefully-made quant at ~5.3 bpw. Our builds are therefore **second-generation quants** (Heretic abliteration → 5.29 bpw master → our 3.0/3.5 bpw), which costs some quality versus a hypothetical GSQ-RCO built straight from the vendor's bf16. That is the highest-fidelity uncensored pipeline available for this model as of today.
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View details ↗InCaseLawBERT
law-ai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗InLegalBERT
law-aiModel and tokenizer files for the InLegalBERT model from the paper Pre-training Transformers on Indian Legal Text. For building the pre-training corpus of Indian legal text, we collected a large corpus of case documents from the Indian Supreme Court and many High Courts of India. The court cases in our dataset range from 1950 to 2019, and belong to all legal domains, such as Civil, Criminal, Constitutional, and so on. In total, our dataset contains around 5.4 million Indian legal documents (all in the English language). The raw text corpus size is around 27 GB. This model is initialized with the LEGAL-BERT-SC model from the paper LEGAL-BERT: The Muppets straight out of Law School. In our work, we refer to this model as LegalBERT, and our re-trained model as InLegalBERT. We further train this model on our data for 300K steps on the Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) tasks.
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