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bge-small-en-MNN

darkmaniac7

- **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

Embeddings & similarity
Input Text→ Output Embedding
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Qwen3.5-4B-MNN

darkmaniac7

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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Visual QA
Input Text · Image→ Output Text
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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**.

Image generation
Input Text→ Output Image
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The FLUX.2 [klein] model family are our fastest image models to date. FLUX.2 [klein] unifies generation and editing in a single compact architecture, **delivering state-of-the-art quality with end-to-end inference in as low as under a second**. Built for applications that require real-time image generation without sacrificing quality, and runs on consumer hardware. FLUX.2 [klein] 9B Base is a 9 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities. It's a full-capacity foundation model. Undistilled, preserving complete training signal for maximum flexibility. Ideal for fine-tuning, LoRA training, research, and custom pipelines where control matters more than speed. Higher output diversity than the distilled models.

Image editing
Input Image→ Output Image
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DaSiWa-WAN2.2-I2V

darksidewalker

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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Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.

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