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AI Models 84759
- SketchThinker-R1-3B-GGUF↗
mradermacher
- typhoon-ocr1.5-2b-i1-GGUF↗
mradermacher
- typhoon2-qwen2vl-7b-vision-instruct-GGUF↗
mradermacher
- ViAble-1.7-9B-i1-GGUF↗
mradermacher
- indic-parler-tts↗
naklitechie
It is a fine-tuned version of Indic Parler-TTS Pretrained, trained on a **1,806 hours** multilingual Indic and English dataset. **Indic Parler-TTS Mini** can officially speak in 20 Indic languages, making it comprehensive for regional language technologies, and in English. The **21 languages** supported are: Assamese, Bengali, Bodo, Dogri, English, Gujarati, Hindi, Kannada, Konkani, Maithili, Malayalam, Manipuri, Marathi, Nepali, Odia, Sanskrit, Santali, Sindhi, Tamil, Telugu, and Urdu. Thanks to its **better prompt tokenizer**, it can easily be extended to other languages. This tokenizer has a larger vocabulary and handles byte fallback, which simplifies multilingual training.
- nemu-ocr-coreml↗
nemu-pm
- Qwen3VL-8B-Abliterated-INT8-ConvRot↗
NidAll
A complete multimodal **Qwen3-VL text encoder for Qwen Image 2.1 Turbo in ComfyUI**, reconstructed from AtomicChat's BF16 GGUF release. Choose the **9.35 GB INT8 ConvRot** checkpoint for reduced storage, or **17.53 GB BF16** for the original reconstructed precision. Both include the entire BF16 vision tower and image-to-language mergers needed for image conditioning. **No separate mmproj is required.** The source modification affects the language encoder; the diffusion transformer and VAE are unchanged. The INT8 checkpoint matches the official Comfy-Org encoder's **exact file size and tensor precision policy**. It is **46.67% smaller than BF16**. Each file is a complete encoder; download one for ordinary use.
- tiny-random-kimi-k3-mxfp4↗
nikita-savelyev-cerebras
- InternVL2_5-1B-MPO↗
OpenGVLab
- OpenMed-NER-ChemicalDetect-SuperClinical-184M↗
OpenMed
**Specialized model for Chemical Entity Recognition - Identifies chemical compounds and substances in biomedical literature** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for chemical entity recognition - identifies chemical compounds and substances in biomedical literature. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as **drug interaction detection**, **medication extraction from patient records**, **adverse event monitoring**, **literature mining for drug discovery**, and **biomedical knowledge graph construction** with **production-ready reliability** for clinical and research applications. - **High Precision**: Optimized for biomedical entity recognition - **Domain-Specific**: Trained on curated BC4CHEMD dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem