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AI 模型 85003
- Zenith_4B-i1-GGUF↗
mradermacher
- cerulean-lab5-distill128-3b-j559819↗
Nat1an
- DeepSeek-V4.1-Flash-1017-EXL3-3bpw-2x-RTX-PRO-6000↗
Nateemerson
- Kronos-Tokenizer-2k↗
NeoQuasar
**Kronos** is the **first open-source foundation model** for financial candlesticks (K-lines), trained on data from over **45 global exchanges**. It is designed to handle the unique, high-noise characteristics of financial data. Kronos is a family of decoder-only foundation models, pre-trained specifically for the "language" of financial markets—K-line sequences. It leverages a novel two-stage framework: 1. A specialized tokenizer first quantizes continuous, multi-dimensional K-line data (OHLCV) into **hierarchical discrete tokens**. 2. A large, autoregressive Transformer is then pre-trained on these tokens, enabling it to serve as a unified model for diverse quantitative tasks. The success of large-scale pre-training paradigm, exemplified by Large Language Models (LLMs), has inspired the development of Time Series Foundation Models (TSFMs). Kronos addresses existing limitations by introducing a specialized tokenizer that discretizes continuous market information into token sequences, preserving both price dynamics and trade activity patterns. We pre-train Kronos using an autoregressive objective on a massive, multi-market corpus of over 12 billion K-line records from 45 global exchanges, enabling it to learn nuanced temporal and cross-asset representations. Kronos excels in a zero-shot setting across a diverse set of financial tasks, including price series forecasting, volatility forecasting, and synthetic data generation.
- gpt4all-j-sharegpt-dolly-epoch_0↗
nomic-ai
- Meta-Llama-3-70B-Instruct↗
NousResearch
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. **Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. **Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
- gpt-audio-mini-2025-10-06↗
OpenAI
- Vlaser-2B-VLA↗
OpenGVLab
- OpenMed-ZeroShot-NER-BloodCancer-Large-459M↗
OpenMed
**Specialized model for Clinical Entity Recognition - Clinical entities related to Chronic Lymphocytic Leukemia** Domain-tuned for **Chronic Lymphocytic Leukemia (CLL)** terminology, capturing disease descriptors, biomarkers, and therapies.Supports **hematology research**, **treatment response analysis**, and **clinical evidence tracking**. OpenMed ZeroShot NER is an advanced, domain-adapted Named Entity Recognition (NER) model designed specifically for medical, biomedical, and clinical text mining. Leveraging state-of-the-art zero-shot learning, this model empowers researchers, clinicians, and data scientists to extract expert-level biomedical entities—such as diseases, chemicals, genes, species, and clinical findings—directly from unstructured text, without the need for task-specific retraining.
- Qwen3-VL-8B-Abliterated-Caption-it-GGUF↗
prithivMLmods
> The Qwen3-VL-8B-Abliterated-Caption-it model is a fine-tuned version of Qwen3-VL-8B-Instruct, tailored for Abliterated Captioning / Uncensored Image Captioning. This variant is designed to generate highly detailed and descriptive captions across a broad range of visual categories, including images with complex, sensitive, or nuanced content—across varying aspect ratios and resolutions. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)