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AI Models 84757

• LFM2.5-24B-A2B-abliterated↗

  notfakerv

• cosmos↗

  novelcore

• geneformer_V2_104M_CLcancer↗

  NVIDIA

• snowball-67b-a2b-base-262k-qk175↗

  open-athena

• OpenMed-NER-GenomicDetect-ElectraMed-33M↗

  OpenMed

  This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for gene entity recognition - gene-related entities. 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 GELLUS dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem Gellus corpus targets gene recognition and genetics entities for genomics and molecular biology applications.

• privacy-filter-nemotron↗

  OpenMed

  Fine-tuned `openai/privacy-filter` for **fine-grained PII extraction** across **55 categories** from `nvidia/Nemotron-PII`. - **Base model**: `openai/privacy-filter` — 1.4B-parameter MoE (50M active per token), BIOES token-classification head - **Task**: Token classification for PII detection (BIOES scheme) - **Training data**: Full 100K rows of `nvidia/Nemotron-PII` train split - **Held-out val**: 10K label-stratified rows from the Nemotron `test` split (every label has ≥229 entities) - **Recipe**: `opf train` (OpenAI's official fine-tuning CLI) — full fine-tune, AdamW, lr=1e-4, 5 epochs, bf16, weight decay 0.0 - **Labels**: 55 fine-grained PII categories → 221 BIOES classes (1 `O` + 55 × B/I/E/S) The base model ships with 8 coarse PII categories (`private_person`, `private_email`, etc.). This model trades that coarse vocabulary for a **5× more granular one** — `first_name`, `last_name`, `medical_record_number`, `credit_debit_card`, `ssn`, and so on — matching what downstream redaction and masking pipelines typically need.

• Qwen2.5-Coder-7B-Instruct-GGUF↗

  orbisaideveloper

• kolors-virtual-try-on-v1↗

  待核实厂商

• krea-2-amd-rocm-optimized-comfy-triton↗

  PuppetVision

• Qwen2.5-Math-1.5B-Instruct↗

  阿里巴巴 · Qwen

  > [!Warning] > > > 🚨 Qwen2.5-Math mainly supports solving English and Chinese math problems through CoT and TIR. We do not recommend using this series of models for other tasks. > > In August 2024, we released the first series of mathematical LLMs - Qwen2-Math - of our Qwen family. A month later, we have upgraded it and open-sourced **Qwen2.5-Math** series, including base models **Qwen2.5-Math-1.5B/7B/72B**, instruction-tuned models **Qwen2.5-Math-1.5B/7B/72B-Instruct**, and mathematical reward model **Qwen2.5-Math-RM-72B**. Unlike Qwen2-Math series which only supports using Chain-of-Thught (CoT) to solve English math problems, Qwen2.5-Math series is expanded to support using both CoT and Tool-integrated Reasoning (TIR) to solve math problems in both Chinese and English. The Qwen2.5-Math series models have achieved significant performance improvements compared to the Qwen2-Math series models on the Chinese and English mathematics benchmarks with CoT.