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TText generation▧Image generation▷Video generation⌘Code generation◖Speech synthesis♫Music generation⬡3D modeling▤Document analysis
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**CodonTransformer** is the ultimate tool for codon optimization, transforming protein sequences into optimized DNA sequences specific for your target organisms. Whether you are a researcher or a practitioner in genetic engineering, CodonTransformer provides a comprehensive suite of features to facilitate your work. By leveraging the Transformer architecture and a user-friendly Jupyter notebook, it reduces the complexity of codon optimization, saving you time and effort. Adibvafa Fallahpour 1,2 \*, Vincent Gureghian 3 \*, Guillaume J. Filion 2 ‡, Ariel B. Lindner 3 ‡, Amir Pandi 3 ‡ 1 Vector Institute for Artificial Intelligence, Toronto ON, Canada 2 University of Toronto Scarborough; Department of Biological Science; Scarborough ON, Canada 3 Université Paris Cité, INSERM U1284, Center for Research and Interdisciplinarity, F-75006 Paris, France \* These authors contributed equally to this work. ‡ To whom correspondence should be addressed: guillaume.filion@utoronto.ca, ariel.lindner@inserm.fr, amir.pandi@cri-paris.org

Text analysis
Input Text→ Output Text
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zeta-2.1-GGUF

adilkairolla

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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Text generation
Input Text→ Output Text
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soccana

Adit-jain

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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Object detection
Input Image→ Output Not verified
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IndicF5

Aditya02

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Speech synthesis
Input Text→ Output Audio
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logibot-slm

adolfo123456

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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Text generation
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**Uncensored 552B MoE with 1M-token context, vision, and full tool/reasoning support — converted to GGUF for llama.cpp.** Built from `dealignai/DeepSeek-V4.1-Flash-UNCENSORED-FP8` (permanent weight-level abliteration of `deepseek-ai/DeepSeek-V4.1-Flash`), converted to GGUF and requantized. **No guardrails: the model follows on prompts the base model refuses (verified, see Benchmarks).** > ## ✅ Update 2026-10-15 — corrected re-quants are live > > **The Q3_K_M and Q4_K_M files on the Hub have been replaced** with corrected re-quants built from the Q8_0 bridge using the corrected per-layer ramp (see below). If you downloaded Q3 or Q4 earlier, the files have changed — re-download. > > - **Q3_K_M:** now **352 GB (9 × ≤50 GB shards)** — 23 GB smaller than the previous build, identical measured quality at every benchmark we ran on both. > - **Q4_K_M:** now **426 GB (3 shards)** — routed experts are pure Q4_K as the tier name says (the previous build re-encoded the checkpoint-native MXFP4 experts into Q4_K/Q5_K, which is what the audit flagged). > - **Q2_K:** rebuilt (v3) — now pins the indexer/hyperconn layers per the recipe (the previous build left them at base precision). Same size class, files replaced on the Hub. The benchmark table below is measured on v3; the 1M NIAH tier on v3 is running. > - **Q8_0:** unchanged — still the reference bridge. > > Details: Per-group quantization & size · Benchmarks

Visual QA
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Category pending
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Category pending
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Image generation
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This is just a full-extract of https://huggingface.co/TenStrip/LTX2.3-10Eros as a LoRA, using the https://github.com/ethanfel/ComfyUI-LoRA-Optimizer suite. I'm using this distilled GGUF from Unsloth for my working tests : https://huggingface.co/unsloth/LTX-2.3-GGUF/blob/main/distilled-1.1/ltx-2.3-22b-distilled-1.1-UD-Q4_K_M.gguf

Video generation
Input Image→ Output Video
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