Llama-3.2-90B-Vision
alpindale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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Llama-Guard-3-1B
alpindaleLlama Guard 3-1B is a fine-tuned Llama-3.2-1B pretrained model for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM – it generates text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated. Llama Guard 3-1B was aligned to safeguard against the MLCommons standardized hazards taxonomy and designed to lower the deployment cost of moderation system safeguard compared to its predecessors. It comes in two versions : 1B and 1B pruned and quantized, optimized for deployment on mobile devices. This repository contains two versions of Llama-Guard-3-1B, for use with transformers and with the original `llama` codebase. Once you have access to the model weights, follow the appropriate section.
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View details ↗WizardLM-2-8x22B
alpindaleWe introduce and opensource WizardLM-2, our next generation state-of-the-art large language models, which have improved performance on complex chat, multilingual, reasoning and agent. New family includes three cutting-edge models: WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B. We also adopt the automatic MT-Bench evaluation framework based on GPT-4 proposed by lmsys to assess the performance of models. The WizardLM-2 8x22B even demonstrates highly competitive performance compared to the most advanced proprietary models. Meanwhile, WizardLM-2 7B and WizardLM-2 70B are all the top-performing models among the other leading baselines at 7B to 70B model scales. We carefully collected a complex and challenging set consisting of real-world instructions, which includes main requirements of humanity, such as writing, coding, math, reasoning, agent, and multilingual. We report the win:loss rate without tie:
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View details ↗frontier_diff_no_cond_try_2
alre5639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 ↗DeepSeek-R1-Distill-Llama-8B
AlSamCur123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 ↗Quen32
AlSamCur123**Example usage**: - For text only LLMs: `llama-cli -hf AlSamCur123/Quen32 --jinja` - For multimodal models: `llama-mtmd-cli -hf AlSamCur123/Quen32 --jinja`
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View details ↗NSFW_RP-3.2-1B
alst10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 ↗qwen3-8b-erotic-stories
alst10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 ↗SEX_ROLEPLAY-3.2-1B
alst10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 ↗bert-base-japanese-emotion-lily
alter-wang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 ↗Fluid-1-Mini-2B-MLX-6bit
altic-dev2B dictation model (Qwen3.5 base, step 7314) quantized to affine Q6 group 64 for the FluidDecode runtime, with the v23 five-layer block-16 DFlash drafter attached, served at block 8 (taps [2, 7, 12, 17, 21], full 98k draft vocabulary, causal block attention). Output is byte-identical to plain greedy decoding. Requires FluidIntelligence with causal DFlash block attention (September 2026 or later).
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View details ↗Fluid-1-Pico-0.8B-MLX-6bit
altic-dev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 ↗FluidIntelligence
altic-dev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 ↗nemotron-3-diarization-coreml
altic-devCore ML export of the final `nvidia/Nemotron-3-Diarization` checkpoint (revision `98fcee8e866bb534eee85d1eb3817170700ae717`), for on-device speaker diarization on macOS 14+ / iOS 17+. Weights are stored as fp16 and widened to fp32 at load time (`constexpr_cast`), so every op computes in fp32. The package stays at fp16 size (~199 MB). Frame-level agreement with NVIDIA's NeMo reference, same streaming loop and post-processing, on real meeting recordings:
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View details ↗CodeRosa-70B-AB1-GGUF
altomek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 ↗YiSM-34B-0rn
altomekThis is Yi Self Merged. I wanted model that will follow most instuctions yet preserve its base model nature. The following table outlines the primary characteristics and intended uses of my YiSM-34B-0rn models: Please remember that all YiSM-34B-0rn models operate under the apache-2.0 license, so familiarize yourself with its terms and conditions before employing their content.
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View details ↗JugnuLM-110M-R1
altslate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 ↗JugnuLM-110M-R2
altslate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 ↗JugnuLM-110M-R2plus
altslate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 ↗JugnuLM-110M-R3
altslate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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