Orion

OrionStarAI/Orion is ranked 13,608 among these 8 peers; VinAIResearch/PhoBERT is ranked 13,598.

Orion-14B Table of Contents 📖 Model Introduction 🔗 Model Download 🔖 Model Benchmark 📊 Model Inference 📜 Declarations & License 🥇 Company Introduction 1. Model Introduction Orion-14B series models are open-source multilingual large language models trained from scratch by OrionStarAI. 5T multilingual corpus, including Chinese, English, Japanese, Korean, etc, and it exhibits superior performance in these languages. For details, please refer to tech report. The Orion-14B series models exhibit the following features: Among models with 20B-parameter scale level, Orion-14B-Base model

shows outstanding performance in comprehensive evaluations. Strong multilingual capabilities, significantly outperforming in Japanese and Korean testsets. The fine-tuned models demonstrate strong adaptability, excelling in human-annotated blind tests. The long-chat version supports extremely long texts, performing exceptionally well at a token length of 200k and can support up to a maximum of 320k. The quantized versions reduce model size by 70%, improve inference speed by 30%, with performance loss less than 1%. Orion-14B series models including: **Orion

-14B-Base:** A multilingual large language foundational model with 14 billion parameters, pretrained on a diverse dataset of 2. **Orion-14B-Chat:** A chat-model fine-tuned on a high-quality corpus aims to provide an excellence interactive experience for users in the large model community. **Orion-14B-LongChat:** The long-context version excels at handling extremely lengthy texts, performing exceptionally well at a token length of 200k and can support up to a maximum of 320k. **Orion-14B-Chat

-RAG:** A chat-model fine-tuned on a custom retrieval augmented generation dataset, achieving superior performance in retrieval augmented generation tasks. **Orion-14B-Chat-Plugin:** A chat-model specifically tailored for plugin and function calling tasks, ideal for agent-related scenarios where the LLM acts as a plugin and function call system. **Orion-14B-Base-Int4:** A quantized base model utilizing 4-bit integer weights. It significantly reduces the model size by 70% and increases the inference speed by 30

% while incurring a minimal performance loss of only 1%. **Orion-14B-Chat-Int4:** A quantized chat model utilizing 4-bit integer weights. Comparison of LLM performances on Korean testsets. 3 | * | * | * | | Weblab-10B | En,Jp | 50. 7 | * | * | * | | ELYZA-jp-7B | En,Jp | 48. 8 | * | * | * | | StableLM-jp-7B | En,Jp | 51. 1 | * | * | * | | KoGPT-6B | En,Ko | * | 70. 1 | * | * | | Polyglot-ko-13B | En,Ko | * | 70. 7 | * | * | | Baichuan2-13B | Multi | 57. 8** | | **Orion-14B-Chat** | Multi | **69. Chat model subjective evaluation of AlignBench | Model

| Math. LongChat Model Orion-14B-LongChat Benchmarks 3. Chat Plugin Model Orion-14B-Chat-Plugin Benchmarks 3. Quantized Model Orion-14B-Base-Int4 Benchmarks 3. Model Inference Model weights, source code, and configuration needed for inference are published on Hugging Face, and the download link is available in the table at the beginning of this document. We demonstrate various inference methods here, and the program will automatically download the necessary resources from Hugging Face. Python Code In the above Python

code, the model is loaded with to utilize all available GPUs. To specify the device, you can use something like