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AI 模型 84759
- LLaDA2.1-mini-ORL↗
yifanyu
- cognitivecomputations_Dolphin-Mistral-24B-Venice-Edition-GGUF↗
Yixman
- UME-R1-7B↗
zhibinlan
- Gemma-4-12B-it-AEON-Abliterated-MLXFP4↗
AEON-7
> **The compact, fast, 16 GB-friendly Apple-Silicon build** of `AEON-7/Gemma-4-12B-it-AEON-Abliterated-K4-BF16`. True 4-bit `mxfp4` on the bulk of the decoder, **8-bit `mxfp8` on the two residual-writers that carry the abliteration edit**, and **bf16** on the soft-capped head and the vision/audio projectors. Built and validated on a **MacBook Pro M4 Pro (48 GB)**. > > **Target hardware: Apple Silicon (M-series), runs on 16 GB+ unified memory** (peaks ~10.1 GB). Full multimodal (text + image + audio) via `mlx-vlm`. > > Want maximum fidelity? See the near-lossless 8-bit sibling: `…-MLX-8bit`. This is the **smallest** member of the MLX quant grid (9.3 GB) and the **fastest single-stream** — a high-quality compact build for Apple Silicon on a tight unified-memory budget. It stays fully coherent, keeps the abliteration intact (**0/8 refusals on the harmful probe**), and preserves the full vision/audio path. For the tightest possible match to BF16, the near-lossless **MLX-8bit** sibling is one click away. **0 → running on a fresh Mac** (no Python, no tools needed) — `uv` installs a correct Python + the deps for you:
- Cheers↗
ai9stars
- indobert_sentiment_news↗
aliffiaaliffia
- GIST-Embedding-v0↗
avsolatorio
*GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning* The model is fine-tuned on top of the BAAI/bge-base-en-v1.5 using the MEDI dataset augmented with mined triplets from the MTEB Classification training dataset (excluding data from the Amazon Polarity Classification task). The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions.
- LLaMA3-iterative-DPO-final-GGUF↗
bartowski
- Qwen3.8-27B-GPTQ-Int4↗
bernhardbrieger
- KOLIBRI-1-DERISKED-NVFP4↗
Blackfrost-AI