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This repository uses the weights from the original DeepSeek-OCR 2 and modifies model to support inference on different devices such as CPU and MPS (Apple Metal GPU). By default runs on CPU. For CUDA, you should also use `bfloat16` to get as close as possible to the original implementation.

Visual QA
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**⚠️ Runs on `ds4` / DwarfStar only — NOT llama.cpp · Ollama · LM Studio · vLLM.** *(Ignore Hugging Face's generic "Use this model" panel — those runtimes fail with `unknown architecture 'glm5next'`.)* An **imatrix-calibrated ~2-bit GGUF** of the *uncensored* GLM-5.3-Flash, built for the **`ds4` / DwarfStar** engine. It runs **resident** on a single 128 GB Mac, keeps the model's **native MTP** speculative-decoding block, and is calibrated with a 1.5M-token importance matrix — not a plain RTN quant. The official uncensored weights ship for **data-center GPUs** (FP8 / NVFP4); a consumer GGUF (Q2_K) also exists, but it runs only on a draft-PR llama.cpp build and drops MTP. This build is the one that runs on **ds4 / Metal today** — resident, with MTP and imatrix calibration:

Text generation
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DogeGPT1-1B

Doge-GPT

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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Category pending
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DOGR

腾讯

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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Category pending
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GGUF quants of Qwen/Qwen-Image-2.1-Turbo, the 8-step distilled Qwen-Image-2.1 (7B DiT). Converted with stable-diffusion.cpp from the official diffusers weights on release day. Every quant is tested on a single RTX 3060 12GB. These are made for **stable-diffusion.cpp** (`sd-cli`, `sd-server`, and anything built on it, like llama-swap). They are not ComfyUI-GGUF files. Using ComfyUI? See the section at the bottom. `Q4_K_S` and `Q3_K_S` are uniform k-quants (every quantized tensor is Q4_K or Q3_K). `qwen-image-2.1-turbo-Q4_K.gguf` and `qwen-image-2.1-turbo-Q3_K.gguf` are the exact same files under their old names, kept so existing links keep working.

Image generation
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This is a compressed version of danielchalef/Qwen3-Reranker-4B-seq-cls-vllm-fixed using llm-compressor with the following scheme: W4A16 ``python3 -m vllm.entrypoints.openai.api_server --model 'dolfsai/Qwen3-Reranker-4B-seq-cls-vllm-W4A16' --task classify``

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Dolphin

字节跳动

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Dolphin-v2

字节跳动

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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**ner-legal-bert-base-cased-ptbr** is a NER model (token classification) in the legal domain in Portuguese that was finetuned from the model dominguesm/legal-bert-base-cased-ptbr by using a NER objective. The model is intended to assist NLP research in the legal field, computer law and legal technology applications. Several legal texts in Portuguese (more information below) were used with the following labels: The labels were inspired by the LeNER_br dataset.

Text analysis
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