orca2

Orca 2

Orca 2由微软研究院构建，是Meta的Llama 2模型的微调版本。该模型特别擅长推理。

7b 13b

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12个月前更新

12个月前

ea98cc422de3 · 3.8GB

{ "num_ctx": 4096, "stop": [ "<|im_start", "<|im_end", "|im_start",

82B

模板

<|im_start|>system {{ .System }}<|im_end|> <|im_start|>user {{ .Prompt }}<|im_end|> <|im_start|>assi

106B

系统

你是Orca，一个由微软创建的AI语言模型。你是一个谨慎的助手。你仔细地

215B

许可证

MICROSOFT RESEARCH LICENSE TERMS IF YOU LIVE IN THE UNITED STATES, PLEASE READ THE “BINDING ARBIT

11kB

自述文件

Orca 2模型由微软研究院构建。它们使用为增强小型模型推理能力而创建的合成数据集对Meta的Llama 2进行了微调。所有合成训练数据都使用Microsoft Azure内容过滤器进行了审核。

用例

Orca 2是一个有帮助的助手，它可以回答诸如根据你的给定数据进行推理、阅读理解、解决数学问题和文本摘要等任务。

该模型特别擅长推理。

微软研究院的预期目的是鼓励对小型语言模型的开发、评估和对齐进行进一步研究。

CLI

70亿参数模型

ollama run orca2

130亿参数模型

ollama run orca2:13b

API

示例

curl -X POST https://127.0.0.1:11434/api/generate -d '{
  "model": "orca2",
  "prompt":"Why is the sky blue?"
 }'

参考文献

微软研究院公告博客
微软研究院发表的论文，Orca 2：教小型语言模型如何推理

Orca 2 models are built by Microsoft Research. They are fine-tuned on Meta's Llama 2 using a synthetic dataset that was created to enhance the small model's reasoning abilities. All synthetic training data was moderated using Microsoft Azure content filters.

### Use case

Orca 2 is a helpful assistant, and provides an answer in tasks such as reasoning over your given data, reading comprehension, math problem solving and text summarization.

The model is designed to excel particularly in reasoning.

Microsoft Research's intended purpose for this model is to encourage further research on the development, evaluation, and alignment of smaller language models.

### CLI

7 billion parameter model:

```
ollama run orca2
```

13 billion parameter model:

```
ollama run orca2:13b
```

### API

Example:

```bash
curl -X POST https://127.0.0.1:11434/api/generate -d '{
  "model": "orca2",
  "prompt":"Why is the sky blue?"
 }'
```

### References

- [Microsoft Research announcement blog](https://www.microsoft.com/en-us/research/blog/orca-2-teaching-small-language-models-how-to-reason/)

- Microsoft Research published paper, [Orca 2: Teaching Small Language Models How to Reason](https://arxiv.org/pdf/2311.11045.pdf)

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