- **Website:** https://tokforge.ai - **Discord:** https://discord.gg/Acv3CBtfVm - **Google Play:** https://play.google.com/store/apps/details?id=dev.tokforge - **iOS TestFlight:** https://testflight.apple.com/join/jnufjzRr MNN-format conversion of BAAI/bge-small-en-v1.5 for use in on-device inference via the MNN runtime (Android / Linux / iOS). Produced for TokForge to consolidate embedding + LLM inference on a single runtime and remove a GGUF/llama.cpp dependency for RAG and memory/reflection embeddings. - Base model: `BAAI/bge-small-en-v1.5` (BERT encoder, 33M params) - Architecture: 12-layer BERT, hidden=384, 12 attention heads, intermediate=1536 - Pooling: **CLS token** (token 0 of `last_hidden_state`), then L2-normalize - Max sequence length: 512 - Embedding dim: 384 - Precision: fp16 weights - File size: 64 MB
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View details ↗A **Q4_0-quantized** single self-contained **GGUF** for `stable-diffusion.cpp`, packaging **DreamShaper-7** (SD1.5, realistic finetune) with the **LCM-LoRA** fused into the UNet for fast, guidance-free **few-step** sampling. This is the **6 GB-tier fast image route** for the TokForge apps, the smaller sibling of `TokForge-DreamShaper-LCM-GGUF` (f16, ~2.1 GB). It renders the same coherent people and hands (no SD-Turbo body-horror) at the same few-step LCM speed class, but at a smaller download + RAM footprint so it fits the 6 GB device tier where the f16 build is gated out. This GGUF was quantized with **stable-diffusion.cpp's own `-M convert --type q4_0`**, whose `tensor_should_be_converted` rule **protects embeddings and norms**; it keeps every tensor whose name contains `embedding` (and all `.bias` / `.scale` / norm tensors) at **F16**. So the CLIP text encoder's `token_embedding.weight` stays **F16**.
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