A 64.8 GB GGUF of Qwen/Qwen3.8-Flash-Next that scores at parity with the full 90 GB quant on tool-calling, GSM8K, and MMLU — 28% smaller, with **zero training and zero transformer surgery**. A 75.4 GB Q4_0 variant of the same cut is also available for older GPUs; see Files. Qwen3.8-Flash-Next carries a 51B-parameter n-gram hash-embedding table (~29% of total weights): 16 independent hash heads, 8 per n-gram type (bigrams / trigrams), injected at layer 1. This model keeps **1 head of 8 per n-gram type** and drops the other 14, exploiting the built-in redundancy of multi-head hashing (each n-gram is looked up several independent ways; a Bloom-filter-style safety margin). Every other tensor is copied **byte-for-byte** from unsloth's UD-Q3_K_XL dynamic quant, so unsloth's imatrix calibration is preserved exactly. The Q4_0 build is **bigger**, not smaller. The n-gram table is what makes this model compact; Q4_0 simply stores every other tensor at more bits than Q3_K_XL does. You are buying throughput with VRAM. On gfx906 that trade is usually worth it — Q4_0 reaches llama.cpp's MMVQ path while K-quants and IQ4_NL fall through to a generic dequant path several times slower.
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TextNet is a lightweight and efficient architecture designed specifically for text detection, offering superior performance compared to traditional models like MobileNetV3. With variants **TextNet-T**, **TextNet-S**, and **TextNet-B** (6.8M, 8.0M, and 8.9M parameters respectively), it achieves an excellent balance between accuracy and inference speed. TextNet achieves state-of-the-art results in text detection, outperforming hand-crafted models in both accuracy and speed. Its architecture is highly efficient, making it ideal for GPU-based applications. We first compare TextNet with representative hand-crafted backbones, such as ResNets and VGG16. For a fair comparison, all models are first pre-trained on IC17-MLT [52] and then finetuned on Total-Text. The proposed TextNet models achieve a better trade-off between accuracy and inference speed than previous hand-crafted models by a significant margin. In addition, notably, our TextNet-T, -S, and -B only have 6.8M, 8.0M, and 8.9M parameters respectively, which are more parameter-efficient than ResNets and VGG16. These results demonstrate that TextNet models are effective for text detection on the GPU device.
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The encoder half of `Contrastive-LM/CLM-v0.1-8B`, quantised for llama.cpp. CLM is a ranker, not a generator: a frozen Qwen3-8B encoder with last-token pooling feeding two frozen projection heads. This repo is the encoder. The heads are **not** quantised and **not** re-shipped — `clm-serve` already fetches `CLM_v0.1-8B.pt` from the parent repo into `~/.cache/clm/`. Every quantised file has **last-token pooling baked in** (`pooling_type = 3`), so no runtime flag is needed. `token_embd.weight` and `lm_head` are held at 8-bit in all of them. `clm-serve` fetches the heads into `~/.cache/clm/` on first run. See the parent model card for the full API.
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This repository provides a **ComfyUI-compatible conversion** of the official TaoLiveAIGC/TaoMate-H3 step-3000 EMA LoRA adapter for **MiniMax H3 (FL2VA)**. The original weights were released in PEFT / Diffusers format with non-standard key naming. This converted version maps all weights directly to ComfyUI's DiT structure, embeds the required alpha scalar tensors, and casts the weights to `bfloat16` to save VRAM and disk space (~1.24 GB vs original ~2.48 GB). When loaded in ComfyUI, all **208 patches** attach cleanly without missing key warnings.
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Finetuned ai-forever/ruT5-base for text and dialogue summarization. - d0rj/samsum-ru - IlyaGusev/gazeta - zjkarina/matreshka - rcp-meetings/rudialogsum_v2 - GEM/wiki_lingua - mlsum > Scheduler, optimizer and trainer states are saved into this repo, so you can use that to continue finetune with your own data with existing gradients.
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