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AI 모델 84999
- OpenMed-PII-ClinicalE5-Base-109M-v1-onnx-android↗
OpenMed
**A 109M BERT model for detecting personal and clinical identifiers in English clinical and biomedical text.** Runs locally after download on Python CPU, in the browser, and on Android. OpenMed selects the CPU-oriented INT8 graph by default and returns labels, confidence scores, exact character offsets, and source text. All graphs use opset 18, dynamic batch and sequence axes, stable tensor names, and source-text offset metadata.
- Qwen3.8-27B-PaoAI-ROCmFP4-STRIX-BALANCED-GGUF↗
PaoAI
- opt-125m-dummy-lora↗
peft-internal-testing
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- TinyR1-32B-Preview↗
qihoo360
- LLaMA3-iterative-DPO-final-GGUF↗
QuantFactory
- Qwen2.5-14B-Instruct-AWQ↗
阿里巴巴 · Qwen
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains. - Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots. - **Long-context Support** up to 128K tokens and can generate up to 8K tokens. - **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. **This repo contains the AWQ-quantized 4-bit instruction-tuned 72B Qwen2.5 model**, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 14.7B - Number of Paramaters (Non-Embedding): 13.1B - Number of Layers: 48 - Number of Attention Heads (GQA): 40 for Q and 8 for KV - Context Length: Full 131,072 tokens and generation 8192 tokens - Please refer to this section for detailed instructions on how to deploy Qwen2.5 for handling long texts. - Quantization: AWQ 4-bit
- Qwen3-VL-235B-A22B-Instruct-FP8↗
阿里巴巴 · Qwen
> This repository contains an FP8 quantized version of the Qwen3-VL-235B-A22B model. The quantization method is fine-grained fp8 quantization with block size of 128, and its performance metrics are nearly identical to those of the original BF16 model. Enjoy! Meet Qwen3-VL — the most powerful vision-language model in the Qwen series to date. This generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities.
- Krea2-Solstice-V2↗
Raxephion
- etri-xainlp_-_llama2-12.8b_lora-dpo_v1-gguf↗
RichardErkhov
- internlm_-_internlm2-math-base-20b-gguf↗
RichardErkhov