stable-beluga

curl -X POST https://127.0.0.1:11434/api/generate -d '{
  "model": "stable-beluga",
  "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 stable-beluga`

注意：如果模型尚未下载，`ollama run` 命令将执行 `ollama pull`。要下载模型而不运行它，请使用 `ollama pull stable-beluga`

内存要求

7b 模型通常需要至少 8GB 的 RAM
13b 模型通常需要至少 16GB 的 RAM
70b 模型通常需要至少 64GB 的 RAM

如果您在更高的量化级别上遇到问题，请尝试使用 q4 模型或关闭任何使用大量内存的其他程序。

模型变体

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 后的数字代表用于量化的位数（例如，q4 表示 4 位量化）。数字越大，模型的准确性越高，但运行速度越慢，需要的内存也越多。

别名
latest, 7b, 7b-q4_0
13b, 13b-q4_0
70b, 70b-q4_0

模型源

Ollama 上的 Stable Beluga 源代码

7b 参数原始来源：Stability AI

13b 参数原始来源：Stability AI

70b 参数原始来源：Stability AI

Stable Beluga is based on Llama 2 and then fine-tuned on an Orca-style dataset. It is available in 7B, 13B, and 70B parameter sizes. It was created by Stability AI.

## Get started with Stable Beluga

The model used in the example below is the Stable Beluga model, with 7b parameters, which is a general-use 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": "stable-beluga",
  "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 stable-beluga`

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 stable-beluga`

## Memory requirements

- 7b models generally require at least 8GB of RAM
- 13b models generally require at least 16GB of RAM
- 70b models generally require at least 64GB 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

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-q4_0 |
| 13b, 13b-q4_0 |
| 70b, 70b-q4_0 |

## Model source

**Stable Beluga source on Ollama**

7b parameters original source:
 [Stability AI](https://hugging-face.cn/stabilityai/StableBeluga-7B)

13b parameters original source:
 [Stability AI](https://hugging-face.cn/stabilityai/StableBeluga-13B)

70b parameters original source:
 [Stability AI](https://hugging-face.cn/stabilityai/StableBeluga2)

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