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View details ↗astroBERT
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View details ↗Zero-To-CAD-Qwen3-VL-2B
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View details ↗adi-qwen3.5-4b-glm5.2-general-GGUF
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View details ↗Qwen3.5-35B-A3B-GGUF
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View details ↗Qwen3.8-27B-GGUF
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View details ↗Radha-9B
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View details ↗SDXL-LORA
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View details ↗cxrmate-2
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View details ↗**Example usage**: - For text only LLMs: `llama-cli -hf aemmeath/NasledieLab-Qwen3.5-0.8B-GPT-6-Astra-Luna-Fable-5.1-Opus-5.5-distill-Karpy-GGUF --jinja` - For multimodal models: `llama-mtmd-cli -hf aemmeath/NasledieLab-Qwen3.5-0.8B-GPT-6-Astra-Luna-Fable-5.1-Opus-5.5-distill-Karpy-GGUF --jinja`
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View details ↗ComfyUI-Model-Backups
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View details ↗sam3.1
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View details ↗SeedVR2-models
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View details ↗stable-audio-open-models
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View details ↗MonkeyOCRv2-B-GGUF
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View details ↗> **The near-lossless Apple-Silicon build** of `AEON-7/Gemma-4-12B-it-AEON-Abliterated-K4-BF16`. Every language-decoder linear is `mxfp8` (8-bit, group 32); the tied soft-capped head and the vision/audio projectors are kept at **bf16**. Built and validated on a **MacBook Pro M4 Pro (48 GB)**. > > **Target hardware: Apple Silicon (M-series), best on 24 GB+ unified memory.** Full multimodal (text + image + audio) via `mlx-vlm`. > > Want the smallest build for a 16 GB Mac? See the compact FP4 sibling: `…-MLXFP4`. This is the **maximum-fidelity** member of the MLX quant grid: measured **top-1 token agreement 0.924 and median KL ≈ 0.002 nats** against the BF16 source on the model's own greedy output — below the perceptual/sampling-noise floor on the typical token. **Near-lossless**, and the recommended build when quality is paramount. **0 → running on a fresh Mac** (no Python, no tools needed) — `uv` installs a correct Python + the deps for you:
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View details ↗> **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:
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