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AI Models 84759

• j-a-r-v-i-s-thinking↗

  NeonFart

• gpt-5-chat-2025-08-07↗

  OpenAI

• internimage_h_22kto1k_640↗

  OpenGVLab

• OpenMed-NER-DNADetect-BigMed-278M↗

  OpenMed

  **Specialized model for Biomedical Entity Recognition - Proteins, DNA, RNA, cell lines, and cell types** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for biomedical entity recognition - proteins, dna, rna, cell lines, and cell types. 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. JNLPBA corpus focuses on biomedical named entity recognition for protein, DNA, RNA, cell line, and cell type entities.

• phi-3.5-moe-tiny-random↗

  optimum-intel-internal-testing

• gpt-5.6-sol-undecillion-6u5go5↗

  oroboros-labs

• drama3-small-GGUF↗

  pentacoxian-dev

  **Built with Fish Audio.** This is a compressed derivative of Fish Audio Drama 3. Fish Audio does not sponsor or endorse it. drama3-small keeps Drama 3's expressive Japanese and English voice cloning: inline tags, English style directions and director's notes. It should fit in 12 GB of VRAM. The 35B-A3B mixture-of-experts backbone was pruned to 45% of its expert slots and quantized to about 3 bits. It was then distilled back towards the original on Japanese and English speech. All samples come from this GGUF in one take, cloned from a 29-second Japanese reference recording of the repo owner's voice (used with consent).

• Ternary-Bonsai-4B-gguf↗

  prism-ml

  Q2_0 encodes each weight as a 2-bit code `q in {0, 1, 2, 3}`, dequantized via `w = (q - 1) * scale`. One 128-element block is 34 bytes (2 bytes FP16 scale + 32 bytes of packed 2-bit codes) for an effective **2.125 bits/weight**. The fourth code point (`q = 3`, reconstructing to `+2 * scale`) is reserved for future extensions; for ternary weights it is unused. Q2_0 is effectively lossless for ternary weights — the ternary values land exactly on three of the four 2-bit code points, so quantize/dequantize is bit-exact in the absence of FP16 scale rounding.

• Canopus-LoRA-Flux-FaceRealism↗

  prithivMLmods

• TranslatePsy-AfriSLM-0.8B-Q4-GGUF↗

  qvac