smart-parking-weights
GaryFer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 84999
wav2vec2-deepfake-voice-detector
garystaffordFine-tuned Wav2Vec2 model for detecting AI-generated speech. Determines if audio was spoken by a human or created by AI text-to-speech/voice cloning software. Fine-tuned Wav2Vec2 transformer for binary audio classification (real vs AI-generated speech). Trained to distinguish authentic human speech from synthetic audio generated by AI text-to-speech and voice cloning services including: **Note:** This model uses transfer learning from a base model already trained for deepfake detection. Fast convergence is expected due to task similarity and TTS engine overlap with the base model's training data.
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View details ↗DynamicRAG-8B
gasolsun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 ↗LFM2.5-8B-A1B-Uncensored-Gaston-GGUF
gaston-parravicini> **First publicly available uncensored/abliterated GGUF of LFM2.5-8B-A1B.** > Base model released May 28, 2026. This release: May 29, 2026. LFM2.5-8B-A1B is Liquid AI's latest edge model — a hybrid **convolution + attention MoE** architecture with: The architecture is **not a standard Transformer**. It combines: - 18 layers of gated short convolutions (LIV blocks) — O(n) complexity - 6 layers of Grouped Query Attention (GQA) — O(n²) for global context - MoE feed-forward with sparse expert routing
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View details ↗Qwen3.6-27B-Abliterated-MTP-GGUF
gaston-parravicini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 ↗Ternary-Bonsai-2-27B-Uncensored-Gaston-GGUF
gaston-parravicini> **The 27B model that every abliteration tool said couldn't be touched — edited anyway, with its brain intact.** > A new liberation technique: refusal-steering **compiled straight into the weights**. No fine-tuning. No reference model. No lobotomy. > > Refusal reflex removed at the weight level · MTP draft head grafted · runs on a single RTX 3090 · **honestly measured** — no inflated numbers. See *How we measured*. Bonsai-2 is built to be **un-abliteratable** by every existing tool: its weights are **ternary and Hadamard-rotated**, and it **cannot be fine-tuned** in llama.cpp (there is no backward pass for its state-space layers). Subtract the refusal direction the normal way and **nothing happens**. This release edits it anyway, with a different method entirely. What that buys you, measured rigorously on a single RTX 3090:
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View details ↗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 ↗Qwen2.5-1.5B-Instruct-RKLLM-v1.2.3
GatekeeperZA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 ↗Qwen3-1.7B-RKLLM-v1.2.3
GatekeeperZA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 ↗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 ↗Qwen3.6-35B-A3b-pruned-v2
gatilin**Qwen3.6 35B-A3B 剪枝版（REAP 0.25）GGUF 量化模型**，混合 SSM + Attention + MoE 架构， 配合 **llama.cpp b10537（macOS ARM64 / Metal）** 全套工具链打包发布——`git clone` / `huggingface-cli download` 拉取后**无需编译、无需安装任何依赖**，直接运行脚本即可启动 OpenAI 兼容服务。 - **开箱即用**：仓库内自带 `llama-server` / `llama-cli` / `llama-bench` 及全部 Metal 动态库（`libggml*.dylib` 等），一键脚本零配置启动。 - **超长上下文**：原生支持 262,144（256K）token 上下文。 - **MoE 高效**：192 专家每 token 仅激活 8 个（约 3B 激活参数），单机即可推理。 - **体积可控**：15.15 GiB，Q3_K_M + Q4_K_S + F32 混合量化。 - **平台**：macOS（Apple Silicon，ARM64），依赖 Metal 后端。 - **内存**：建议 ≥ 32 GiB 统一内存；模型权重 15.15 GiB，另需 KV cache（256K 上下文下显著，请按内存下调 `-c`）。 - 非 macOS 平台可忽略 `bin/`，改用任意支持 GGUF 的 llama.cpp 构建自行加载权重。
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View details ↗Youtu-FT-V1-35B-A3B
gatilin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 ↗Youtu-FT-V1-9B
gatilin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 ↗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 ↗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-L4-H128-uncased
gaunernst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-L6-H768-uncased
gaunernstMini BERT models from https://arxiv.org/abs/1908.08962 that the HF team didn't convert. The original conversion script is used.
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View details ↗bert-mini-uncased
gaunernst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-V2-Lite-Chat-FP8
gaunernstThis model was converted from https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat to Block FP8 quantization (128x128) to be used as testing for DeepSeek-V3/R1. Dense's `intermediate_size` was padded to be a multiple of 128. Last week, the release and buzz around DeepSeek-V2 have ignited widespread interest in MLA (Multi-head Latent Attention)! Many in the community suggested open-sourcing a smaller MoE model for in-depth research. And now DeepSeek-V2-Lite comes out: DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. DeepSeek-V2 adopts innovative architectures including Multi-head Latent Attention (MLA) and DeepSeekMoE. MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation.
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View details ↗gemma-3-12b-it-int4-awq
gaunernstThis is the QAT INT4 Flax checkpoint (from Kaggle) converted to HF+AWQ format for ease of use. AWQ was NOT used for quantization. You can find the conversion script `convert_flax.py` in this model repo. NOTE: this is NOT the same as the official QAT INT4 GGUFs released here https://huggingface.co/collections/google/gemma-3-qat-67ee61ccacbf2be4195c265b Below is the original Model card from https://huggingface.co/google/gemma-3-12b-it
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View details ↗gemma-3-27b-it-int4-awq
gaunernstThis is the QAT INT4 Flax checkpoint (from Kaggle) converted to HF+AWQ format for ease of use. AWQ was NOT used for quantization. You can find the conversion script `convert_flax.py` in this model repo. NOTE: this is NOT the same as the official QAT INT4 GGUFs released here https://huggingface.co/collections/google/gemma-3-qat-67ee61ccacbf2be4195c265b Below is the original Model card from https://huggingface.co/google/gemma-3-27b-it
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View details ↗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 ↗gemma-3-4b-it-int4-awq
gaunernst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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