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AI 模型 84997
- DeepHermes-3-Mistral-24B-Preview-GGUF↗
NousResearch
- NVIDIA-Nemotron-3.5-Bolt-E4B-BF16↗
NVIDIA
**NVIDIA-Nemotron-3.5-Bolt-E4B-BF16** is a small language model (SLM) further post-trained from Gemma-4-E4B-it by NVIDIA. This is the full-precision (BF16) release. Nemotron-3.5-Bolt-E4B carries forward Gemma’s strong conversational capabilities and further improves multi-turn tool use, instruction following, and input grounding for fast local voice/device assistants, interactive personas, and embedded/edge interfaces. Designed for low-memory, low-TTFT operation, the model allows agents to respond quickly while preserving headroom for applications or adjacent model pipelines. For speculative decoding, use the companion DFlash drafter. For the Q4_K_M quantized version, see NVIDIA-Nemotron-3.5-Bolt-E4B-4GB. NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents.
- stt_en_fastconformer_ctc_xxlarge↗
NVIDIA
- OpenMed-NER-ChemicalDetect-TinyMed-65M↗
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. BC4CHEMD is a biomedical NER corpus for chemical entity recognition from the BioCreative IV challenge.
- OpenMed-PII-ClinicDischarge-Base-110M-v1↗
OpenMed
**OpenMed-PII-ClinicDischarge-110M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection** in text. This model identifies and classifies **54 types of sensitive information** including names, addresses, SSNs, medical record numbers, and more. ⚠️ **Important**: This model is intended as an **assistive tool**, not a replacement for human review.
- tiny-random-z-image↗
optimum-intel-internal-testing
- Llama-3.1-8B-Instruct-pearl↗
pearl-ai
Pearl-certified variant of Llama-3.1-8B-Instruct, intended to run with the Pearl vLLM mining plugin. This model is intended to be served through the Pearl miner stack, where vLLM inference is integrated with Pearl mining workflows. Typical flow: 1. Run `pearld` with RPC enabled. 2. Start the Pearl miner/vLLM stack. 3. Serve this model through vLLM while Pearl gateway/miner components handle mining-side integration.
- plamo-2-1b↗
pfnet
- Pleias-RAG-1B-gguf↗
PleIAs
- opt-125m-bnb-4bit↗
poedator