nous-hermes:13b-llama2-q5_0

curl -X POST https://127.0.0.1:11434/api/generate -d '{
  "model": "nous-hermes",
  "prompt":"Explain the process of how a refrigerator works to keep the contents inside cold."
 }'

CLI

安装 Ollama
打开终端并运行 ollama run nous-hermes

注意：如果模型尚未下载，ollama run 命令会执行 ollama pull。要下载模型而不运行它，请使用 ollama pull nous-hermes

内存需求

7b 模型通常至少需要 8GB 的 RAM
13b 模型通常至少需要 16GB 的 RAM

如果您在使用更高量化级别的模型时遇到问题，请尝试使用 q4 模型或关闭任何其他正在使用大量内存的程序。

模型变体

Ollama 提供了多种 Nous Hermes 模型变体，这些变体基于官方模型进行量化，以便在本地良好运行。

**Nous Hermes Llama 2** 是基于原始 Llama 模型的原始 Nous Hermes 模型。

示例：ollama run nous-hermes

**Nous Hermes Llama 1** 是基于原始 Llama 模型的原始 Nous Hermes 模型。

示例：ollama run nous-hermes:13b-q4_0

默认情况下，Ollama 使用 4 位量化。要尝试其他量化级别，请尝试其他标签。q 后面的数字表示用于量化的位数（例如，q4 表示 4 位量化）。数字越大，模型越准确，但运行速度越慢，需要的内存也越多。

别名
latest, 7b, 7b-llama2, 7b-llama2-q4_0
13b, 13b-llama2, 13b-llama2-q4_0

模型来源

Ollama 上的 Nous Hermes Llama 2 源代码

7b 参数原始来源：Nous Research

13b 参数原始来源：Nous Research

Ollama 上的 Nous Hermes Llama 1 源代码

13b 参数原始来源：Nous Research

Nous Hermes was released by Nous Research. There are two main variants here, a 13B parameter model based on Llama, and a 7B and 13B parameter model based on Llama 2. They are all general-use models trained with the same datasets.

## Get started with Nous Hermes

The model used in the example below is the Nous Hermes Llama 2 model, with 7b parameters, which is a general chat model.

### API

1. Start Ollama server (Run `ollama serve`)
2. Run the model

```bash
curl -X POST https://127.0.0.1:11434/api/generate -d '{
  "model": "nous-hermes",
  "prompt":"Explain the process of how a refrigerator works to keep the contents inside cold."
 }'
  ```

### CLI

1. Install Ollama
2. Open the terminal and run `ollama run nous-hermes`

Note: The `ollama run` command performs an `ollama pull` if the model is not already downloaded. To download the model without running it, use `ollama pull nous-hermes`

## Memory requirements

- 7b models generally require at least 8GB of RAM
- 13b models generally require at least 16GB of RAM

If you run into issues with higher quantization levels, try using the q4 model or shut down any other programs that are using a lot of memory.

## Model variants

Ollama offers many variants of the Nous Hermes model that are quantized based on the official models to run well locally.

**Nous Hermes Llama 2** is the original Nous Hermes model based on the original Llama model.

*Example: `ollama run nous-hermes`*

**Nous Hermes Llama 1** is the original Nous Hermes model based on the original Llama model.

*Example: `ollama run nous-hermes:13b-q4_0`*

By default, Ollama uses 4-bit quantization. To try other quantization levels, please try the other tags. The number after the q represents the number of bits used for quantization (i.e. q4 means 4-bit quantization). The higher the number, the more accurate the model is, but the slower it runs, and the more memory it requires.

| **Aliases** |
| --- |
| latest, 7b, 7b-llama2, 7b-llama2-q4_0 |
| 13b, 13b-llama2, 13b-llama2-q4_0 |

## Model source

**Nous Hermes Llama 2 source on Ollama**

7b parameters original source:
 [Nous Research](https://hugging-face.cn/NousResearch/Nous-Hermes-llama-2-7b)

13b parameters original source:
 [Nous Research](https://hugging-face.cn/NousResearch/Nous-Hermes-Llama2-13b)

**Nous Hermes Llama 1 source on Ollama**

13b parameters original source:
 [Nous Research](https://hugging-face.cn/NousResearch/Nous-Hermes-13b)

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