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--- |
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license: other |
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license_name: orion |
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license_link: https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/main/ModelsCommunityLicenseAgreement |
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widget: |
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- text: "Hi!" |
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output: |
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text: "Hello! How can I help you today?" |
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pipeline_tag: text-generation |
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--- |
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<!-- markdownlint-disable first-line-h1 --> |
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<!-- markdownlint-disable html --> |
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<div align="center"> |
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<h1> |
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Orion-14B |
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</h1> |
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</div> |
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<div align="center"> |
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<h4 align="center"> |
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<p> |
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<b>🌐English</b> | |
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<a href="https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/main/README_cn.md">🇨🇳中文</a><br><br> |
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🤗 <a href="https://huggingface.co/OrionStarAI" target="_blank">HuggingFace Mainpage</a> | 🤖 <a href="https://modelscope.cn/organization/OrionStarAI" target="_blank">ModelScope Mainpage</a><br>🎬 <a href="https://huggingface.co/spaces/OrionStarAI/Orion-14B-App-Demo" target="_blank">HuggingFace Demo</a> | 🎫 <a href="https://modelscope.cn/studios/OrionStarAI/Orion-14B-App-Demo/summary" target="_blank">ModelScope Demo</a> |
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<p> |
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</h4> |
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</div> |
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# Table of Contents |
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- [📖 Model Introduction](#model-introduction) |
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- [🔗 Model Download](#model-download) |
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- [🔖 Model Benchmark](#model-benchmark) |
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- [📊 Model Inference](#model-inference) |
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- [🥇 Company Introduction](#company-introduction) |
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- [📜 Declarations & License](#declarations-license) |
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# Model Introduction |
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- Orion-14B-Chat is fine-tuned from Orion-14B-Base using a high-quality corpus of approximately 850,000 entries (only sft), and it also supports Chinese, English, Japanese, and Korean. It performs exceptionally well on the MT-Bench and AlignBench evaluation sets, significantly surpassing other models of the same parameter scale in multiple metrics. |
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- The 850,000 fine-tuning corpus comprises two parts: approximately 220,000 manually curated high-quality datasets and 630,000 entries selected and semantically deduplicated from open-source data through model filtering. Among these, the Japanese and Korean data, totaling 70,000 entries, have only undergone basic cleaning and deduplication. |
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- The Orion-14B series models exhibit the following features: |
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- Among models with 20B-parameter scale level, Orion-14B-Base model shows outstanding performance in comprehensive evaluations. |
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- Strong multilingual capabilities, significantly outperforming in Japanese and Korean testsets. |
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- The fine-tuned models demonstrate strong adaptability, excelling in human-annotated blind tests. |
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- The long-chat version supports extremely long texts, extending up to 200K tokens. |
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- The quantized versions reduce model size by 70%, improve inference speed by 30%, with performance loss less than 1%. |
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- Orion-14B series models including: |
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- **Orion-14B-Base:** A multilingual large language foundational model with 14 billion parameters, pretrained on a diverse dataset of 2.5 trillion tokens. |
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- **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. |
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- **Orion-14B-LongChat:** This model is optimized for long context lengths more than 200k tokens and demonstrates performance comparable to proprietary models on long context evaluation sets. |
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- **Orion-14B-Chat-RAG:** A chat-model fine-tuned on a custom retrieval augmented generation dataset, achieving superior performance in retrieval augmented generation tasks. |
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- **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. |
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- **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%. |
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- **Orion-14B-Chat-Int4:** A quantized chat model utilizing 4-bit integer weights. |
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# Model Download |
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Model release and download links are provided in the table below: |
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| Model Name | HuggingFace Download Links | ModelScope Download Links | |
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|-------------------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------| |
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| ⚾Orion-14B-Base | [Orion-14B-Base](https://huggingface.co/OrionStarAI/Orion-14B-Base) | [Orion-14B-Base](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base/summary) | |
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| 😛Orion-14B-Chat | [Orion-14B-Chat](https://huggingface.co/OrionStarAI/Orion-14B-Chat) | [Orion-14B-Chat](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat/summary) | |
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| 📃Orion-14B-LongChat | [Orion-14B-LongChat](https://huggingface.co/OrionStarAI/Orion-14B-LongChat) | [Orion-14B-LongChat](https://modelscope.cn/models/OrionStarAI/Orion-14B-LongChat/summary) | |
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| 🔎Orion-14B-Chat-RAG | [Orion-14B-Chat-RAG](https://huggingface.co/OrionStarAI/Orion-14B-Chat-RAG) | [Orion-14B-Chat-RAG](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-RAG/summary) | |
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| 🔌Orion-14B-Chat-Plugin | [Orion-14B-Chat-Plugin](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Plugin) | [Orion-14B-Chat-Plugin](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Plugin/summary) | |
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| 💼Orion-14B-Base-Int4 | [Orion-14B-Base-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Base-Int4) | [Orion-14B-Base-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base-Int4/summary) | |
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| 📦Orion-14B-Chat-Int4 | [Orion-14B-Chat-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Int4) | [Orion-14B-Chat-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Int4/summary) | |
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# Model Benchmarks |
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## 1. Base Model Benchmarks |
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### LLM evaluation results on examination and professional knowledge |
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| Model | C-Eval | CMMLU | MMLU | AGIEval | Gaokao | BBH | |
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|--------------------|----------|----------|----------|----------|----------|----------| |
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| LLaMA2-13B | 41.4 | 38.4 | 55.0 | 30.9 | 18.2 | 45.6 | |
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| Skywork-13B | 59.1 | 61.4 | 62.7 | 43.6 | 56.1 | 48.3 | |
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| Baichuan2-13B | 59.0 | 61.3 | 59.5 | 37.4 | 45.6 | 49.0 | |
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| QWEN-14B | 71.7 | 70.2 | 67.9 | 51.9 | **62.5** | 53.7 | |
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| InternLM-20B | 58.8 | 59.0 | 62.1 | 44.6 | 45.5 | 52.5 | |
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| **Orion-14B-Base** | **72.9** | **70.6** | **69.9** | **54.7** | 62.1 | **56.5** | |
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### LLM evaluation results on language understanding and common knowledge |
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| Model |RACE-middle|RACE-high |HellaSwag | PIQA | Lambada | WSC | |
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|--------------------|----------|----------|----------|----------|----------|----------| |
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| LLaMA 2-13B | 63.0 | 58.9 | 77.5 | 79.8 | 76.5 | 66.3 | |
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| Skywork-13B | 87.6 | 84.1 | 73.7 | 78.3 | 71.8 | 66.3 | |
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| Baichuan 2-13B | 68.9 | 67.2 | 70.8 | 78.1 | 74.1 | 66.3 | |
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| QWEN-14B | 93.0 | 90.3 | **80.2** | 79.8 | 71.4 | 66.3 | |
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| InternLM-20B | 86.4 | 83.3 | 78.1 | **80.3** | 71.8 | 68.3 | |
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| **Orion-14B-Base** | **93.3** | **91.3** | 78.5 | 79.5 | **78.9** | **70.2** | |
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### LLM evaluation results of OpenCompass testsets |
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| Model | Average | Examination | Language | Knowledge | Understanding | Reasoning | |
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|------------------|----------|----------|----------|----------|----------|----------| |
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| LLaMA 2-13B | 47.3 | 45.2 | 47.0 | 58.3 | 50.9 | 43.6 | |
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| Skywork-13B | 53.6 | 61.1 | 51.3 | 52.7 | 64.5 | 45.2 | |
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| Baichuan 2-13B | 49.4 | 51.8 | 47.5 | 48.9 | 58.1 | 44.2 | |
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| QWEN-14B | 62.4 | 71.3 | 52.67 | 56.1 | 68.8 | 60.1 | |
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| InternLM-20B | 59.4 | 62.5 | 55.0 | **60.1** | 67.3 | 54.9 | |
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|**Orion-14B-Base**| **64.4** | **71.4** | **55.0** | 60.0 | **71.9** | **61.6** | |
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### Comparison of LLM performances on Japanese testsets |
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| Model |**Average**| JCQA | JNLI | MARC | JSQD | JQK | XLS | XWN | MGSM | |
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|--------------------|----------|----------|----------|----------|----------|----------|----------|----------|----------| |
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| PLaMo-13B | 52.3 | 56.7 | 42.8 | 95.8 | 70.6 | 71.0 | 8.70 | 70.5 | 2.40 | |
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| WebLab-10B | 50.7 | 66.6 | 53.7 | 82.1 | 62.9 | 56.2 | 10.0 | 72.0 | 2.40 | |
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| ELYZA-jp-7B | 48.8 | 71.7 | 25.3 | 86.6 | 70.8 | 64.1 | 2.50 | 62.1 | 7.20 | |
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| StableLM-jp-7B | 51.1 | 33.4 | 43.3 | **96.7** | 70.6 | 78.1 | 10.7 | 72.8 | 2.80 | |
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| LLaMA 2-13B | 46.3 | 75.0 | 47.6 | 38.8 | 76.1 | 67.7 | 18.1 | 63.2 | 10.4 | |
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| Baichuan 2-13B | 57.1 | 73.7 | 31.3 | 91.6 | 80.5 | 63.3 | 18.6 | 72.2 | 25.2 | |
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| QWEN-14B | 65.8 | 85.9 | 60.7 | 97.0 | 83.3 | 71.8 | 18.8 | 70.6 | 38.0 | |
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| Yi-34B | 67.1 | 83.8 | 61.2 | 95.2 | **86.1** | 78.5 | **27.2** | 69.2 | 35.2 | |
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| **Orion-14B-Base** | **69.1** | **88.2** | **75.8** | 94.1 | 75.7 | **85.1** | 17.3 | **78.8** | **38.0** | |
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### Comparison of LLM performances on Korean testsets. n = 0 and n = 5 stand for n-shot prompts used in the evaluation |
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|Model | **Average**<br>n=0 n=5 | HellaSwag<br>n=0 n=5 | COPA<br> n=0 n=5 | BooIQ<br>n=0 n=5 | SentiNeg<br>n=0 n=5| |
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|------------------|------------------------------|------------------------------|------------------------------|------------------------------|------------------------------| |
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| KoGPT | 53.0 70.1 | 55.9 58.3 | 73.5 72.9 | 45.1 59.8 | 37.5 89.4 | |
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| Polyglot-ko-13B | 69.6 73.7 |**59.5** **63.1**|**79.4** **81.1**| 48.2 60.4 | 91.2 90.2 | |
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| LLaMA 2-13B | 46.7 63.7 | 41.3 44.0 | 59.3 63.8 | 34.9 73.8 | 51.5 73.4 | |
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| Baichuan 2-13B | 52.1 58.7 | 39.2 39.6 | 60.6 60.6 | 58.4 61.5 | 50.3 72.9 | |
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| QWEN-14B | 53.8 73.7 | 45.3 46.8 | 64.9 68.9 | 33.4 83.5 | 71.5 95.7 | |
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| Yi-34B | 54.2 72.1 | 44.6 44.7 | 58.0 60.6 | 65.9 90.2 | 48.3 92.9 | |
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|**Orion-14B-Chat**|**74.5** **79.6**| 47.0 49.6 | 77.7 79.4 |**81.6** **90.7**|**92.4** **98.7**| |
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### Multilingual evaluation |
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| Model | Train Lang | Japanese | Korean | Chinese | English | |
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|--------------------|------------|----------|----------|----------|----------| |
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| PLaMo-13B | En,Jp | 52.3 | * | * | * | |
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| Weblab-10B | En,Jp | 50.7 | * | * | * | |
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| ELYZA-jp-7B | En,Jp | 48.8 | * | * | * | |
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| StableLM-jp-7B | En,Jp | 51.1 | * | * | * | |
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| KoGPT-6B | En,Ko | * | 70.1 | * | * | |
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| Polyglot-ko-13B | En,Ko | * | 70.7 | * | * | |
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| Baichuan2-13B | Multi | 57.1 | 58.7 | 50.8 | 57.1 | |
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| Qwen-14B | Multi | 65.8 | 73.7 | 64.5 | 65.4 | |
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| Llama2-13B | Multi | 46.3 | 63.7 | 41.4 | 55.3 | |
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| Yi-34B | Multi | 67.1 | 72.2 | 58.7 | **68.8** | |
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| **Orion-14B-Chat** | Multi | **69.1** | **79.5** | **67.9** | 67.3 | |
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## 2. Chat Model Benchmarks |
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### Chat model subjective evaluation of MTBench |
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| Model | First-Turn | Second-Turn | **Average** | |
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|----------------------|----------|----------|----------| |
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| Baichuan2-13B-Chat | 7.05 | 6.47 | 6.76 | |
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| Qwen-14B-Chat | 7.30 | 6.62 | 6.96 | |
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| Llama2-13B-Chat | 7.10 | 6.20 | 6.65 | |
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| InternLM-20B-Chat | 7.03 | 5.93 | 6.48 | |
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| **Orion-14B-Chat** | **7.68** | **7.07** | **7.37** | |
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\* use vllm for inference |
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### Chat model subjective evaluation of AlignBench |
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| Model | Math. | Logi. | Basic. | Chi. | Comp. | Writ. | Role. | Prof. |**Avg.**| |
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|--------------------|--------|--------|--------|--------|--------|--------|--------|--------|--------| |
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| Baichuan2-13B-Chat | 3.76 | 4.07 | 6.22 | 6.05 | 7.11 | 6.97 | 6.75 | 6.43 | 5.25 | |
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| Qwen-14B-Chat |**4.91**|**4.71**|**6.90**| 6.36 | 6.74 | 6.64 | 6.59 | 6.56 |**5.72**| |
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| Llama2-13B-Chat | 3.05 | 3.79 | 5.43 | 4.40 | 6.76 | 6.63 | 6.99 | 5.65 | 4.70 | |
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| InternLM-20B-Chat | 3.39 | 3.92 | 5.96 | 5.50 |**7.18**| 6.19 | 6.49 | 6.22 | 4.96 | |
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| **Orion-14B-Chat** | 4.00 | 4.24 | 6.18 |**6.57**| 7.16 |**7.36**|**7.16**|**6.99**| 5.51 | |
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\* use vllm for inference |
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## 3. LongChat Model Benchmarks |
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### LongChat evaluation of LongBench |
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| Model | NarrativeQA|MultiFieldQA-en|MultiFieldQA-zh| DuReader | QMSum | VCSUM | TREC | TriviaQA | LSHT |RepoBench-P| |
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|--------------------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------| |
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| GPT-3.5-Turbo-16k | **23.60** | **52.30** | **61.20** | 28.70 | 23.40 | **16.00** | 68.00 | **91.40** | 29.20 | 53.60 | |
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| LongChat-v1.5-7B-32k | 16.90 | 41.40 | 29.10 | 19.50 | 22.70 | 9.90 | 63.50 | 82.30 | 23.20 | 55.30 | |
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| Vicuna-v1.5-7B-16k | 19.40 | 38.50 | 43.00 | 19.30 | 22.80 | 15.10 | 71.50 | 86.20 | 28.80 | 43.50 | |
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| Yi-6B-200K | 14.11 | 36.74 | 22.68 | 14.01 | 20.44 | 8.08 | 72.00 | 86.61 | 38.00 | **63.29** | |
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| Orion-14B-LongChat | 19.47 | 48.11 | 55.84 | **37.02** | **24.87** | 15.44 | **77.00** | 89.12 | **45.50** | 54.31 | |
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## 4. Chat RAG Model Benchmarks |
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### LLM evaluation results of self-built RAG testsets |
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|Model|Effectiveness of Response(Keyword)|*Effectiveness of Response（subjective evaluation）|Quoting Ability|Fallback Ability|*AutoQA|*Data Extraction| |
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|---------------------|------|------|------|------|------|------| |
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| Baichuan2-13B-Chat | 85 | 76 | 1 | 0 | 69 | 51 | |
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| Qwen-14B-Chat | 79 | 77 | 75 | 47 | 68 | 72 | |
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| Qwen-72B-Chat(Int4) | 87 | 89 | 90 | 32 | 67 | 76 | |
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| GPT-4 | 91 | 94 | 96 | 95 | 75 | 86 | |
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| Orion-14B-Chat-RAG | 86 | 87 | 91 | 97 | 73 | 71 | |
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\* means manual assessment |
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## 5. Chat Plugin Model Benchmarks |
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### LLM evaluation results of self-built plugin testsets |
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|Model |Intent Recognition with Full Params |Intent Recognition with Missing Params |Non-Plugin Invocation Recognition | |
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|-----------------------|--------|-----------|--------| |
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| Baichuan2-13B-Chat | 25 | 0 | 0 | |
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| Qwen-14B-Chat | 55 | 0 | 50 | |
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| GPT-4 | **95** | 52.38 | 70 | |
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| Orion-14B-Chat-Plugin | 92.5 | **60.32** | **90** | |
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## 6. Quantized Model Benchmarks |
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### Comparison of before and after quantization |
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|Model |Size(GB)|Inference Speed(tokens/s)|C-Eval|CMMLU|MMLU|RACE|HellaSwag| |
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|-------------------------|-------|-----|------|------|------|------|------| |
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| OrionStar-14B-Base | 28.0 | 135 | 72.8 | 70.6 | 70.0 | 93.3 | 78.5 | |
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| OrionStar-14B-Base-Int4 | 8.3 | 178 | 71.8 | 69.8 | 69.2 | 93.1 | 78.0 | |
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# Model Inference |
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Model weights, source code, and configuration needed for inference are published on Hugging Face, and the download link |
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is available in the table at the beginning of this document. We demonstrate various inference methods here, and the |
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program will automatically download the necessary resources from Hugging Face. |
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## Python Code |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from transformers.generation.utils import GenerationConfig |
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tokenizer = AutoTokenizer.from_pretrained("OrionStarAI/Orion-14B", use_fast=False, trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained("OrionStarAI/Orion-14B", device_map="auto", |
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torch_dtype=torch.bfloat16, trust_remote_code=True) |
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model.generation_config = GenerationConfig.from_pretrained("OrionStarAI/Orion-14B") |
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messages = [{"role": "user", "content": "Hello, what is your name? "}] |
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response = model.chat(tokenizer, messages, streaming=False) |
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print(response) |
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``` |
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In the above Python code, the model is loaded with `device_map='auto'` to utilize all available GPUs. To specify the |
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device, you can use something like `export CUDA_VISIBLE_DEVICES=0,1` (using GPUs 0 and 1). |
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## Command Line Tool |
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```shell |
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CUDA_VISIBLE_DEVICES=0 python cli_demo.py |
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``` |
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This command-line tool is designed for chat scenarios, and thus, it does not support calling the base model. |
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## Direct Script Inference |
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```shell |
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# base model |
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CUDA_VISIBLE_DEVICES=0 python demo/text_generation_base.py --model OrionStarAI/Orion-14B --tokenizer OrionStarAI/Orion-14B --prompt hello |
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# chat model |
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CUDA_VISIBLE_DEVICES=0 python demo/text_generation.py --model OrionStarAI/Orion-14B-Chat --tokenizer OrionStarAI/Orion-14B-Chat --prompt hi |
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``` |
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# Example Output |
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## Casual Chat |
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````` |
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User: Hello |
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Orion-14B: Hello! How can I help you today? If you have any questions or need assistance, feel free to ask. |
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User: What functions do you have? |
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Orion-14B: As an AI language model, I have a wide range of functions and capabilities, including but not limited to: |
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Answering general knowledge questions: I can provide information on various topics such as history, science, geography, and more. |
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Providing definitions and explanations: I can explain complex concepts and provide definitions for words and phrases. |
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Assisting with tasks: I can help with simple tasks like setting reminders, converting units of measurement, and even ordering food from restaurants. |
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Generating text: I can write short stories, essays, articles, and other types of content based on prompts given to me. |
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Translating languages: I can translate text from one language to another. |
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Chatting: I can engage in conversation with users and respond to their queries in a conversational manner. |
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Summarizing long texts: I can summarize longer texts like articles, books, and research papers. |
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Generating random numbers and names: I can generate random numbers within a specified range and also suggest names based on certain criteria. |
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Playing games: I can play simple games like Hangman, Trivia, and more. |
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Providing recommendations: I can recommend movies, music, books, and other forms of entertainment based on user preferences. |
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These are just some of the many functions that I am capable of performing. If you have a specific task in mind, feel free to ask me about it. |
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User: Tell me a joke. |
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Orion-14B: Sure, here's a classic one-liner: Why don't scientists trust atoms? Because they make up everything. |
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````` |
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## Japanese & Korean Chat |
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````` |
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User：自己を紹介してください |
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Orion-14B：こんにちは、私の名前はChatMaxで、OrionStarによって開発されたAIアシスタントです。私はユーザーに正確で、客観的で、有用な回答を提供し、彼らが問題を解決し、情報を取得するのを助けることができます。 |
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User：자기소개를 해주세요. |
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Orion-14B：안녕하세요, 제 이름은 ChatMax입니다. OrionStar에서 개발한 AI 어시스턴트입니다. 저는 사용자들에게 정확하고 객관적이며 유용한 답변을 제공하여 문제를 해결하고 정보를 얻는 데 도움을 줄 수 있습니다. |
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````` |
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# Company Introduction |
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OrionStar is a leading global service robot solutions company, founded in September 2016. OrionStar is dedicated to |
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using artificial intelligence technology to create the next generation of revolutionary robots, allowing people to break |
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free from repetitive physical labor and making human work and life more intelligent and enjoyable. Through technology, |
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OrionStar aims to make society and the world a better place. |
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OrionStar possesses fully self-developed end-to-end artificial intelligence technologies, such as voice interaction and |
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visual navigation. It integrates product development capabilities and technological application capabilities. Based on |
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the Orion robotic arm platform, it has launched products such as OrionStar AI Robot Greeting, AI Robot Greeting Mini, |
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Lucki, Coffee Master, and established the open platform OrionOS for Orion robots. Following the philosophy of "Born for |
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Truly Useful Robots", OrionStar empowers more people through AI technology. |
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# Declarations, License |
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## Declarations |
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We strongly urge all users not to use the Orion-14B model for any activities that may harm national or social security or violate the law. |
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Additionally, we request users not to use the Orion-14B model for internet services without proper security review and filing. |
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We hope all users abide by this principle to ensure that technological development takes place in a regulated and legal environment. |
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We have done our best to ensure the compliance of the data used in the model training process. However, despite our |
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significant efforts, unforeseen issues may still arise due to the complexity of the model and data. Therefore, if any |
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problems arise due to the use of the Orion-14B open-source model, including but not limited to data security |
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issues, public opinion risks, or any risks and issues arising from the model being misled, abused, disseminated, or |
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improperly utilized, we will not assume any responsibility. |
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## License |
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Community use of the Orion-14B series models |
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- For code, please comply with [Apache License Version 2.0](./LICENSE)<br> |
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- For model, please comply with [【Orion-14B Series】 Models Community License Agreement](./ModelsCommunityLicenseAgreement) |
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# Contact Us |
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Email: [email protected] |
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