wizard-vicuna-uncensored

curl -X POST https://127.0.0.1:11434/api/generate -d '{
  "model": "wizard-vicuna-uncensored",
  "prompt":"Who made Rose promise that she would never let go?"
 }'

CLI

安装 Ollama
打开终端并运行 ollama run wizard-vicuna-uncensored

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 wizard-vicuna-uncensored

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
30b 模型通常至少需要 32GB 的 RAM

如果在更高的量化级别上遇到问题，请尝试使用 q4 模型或关闭任何其他使用大量内存的程序。

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-q4_0
13b, 13b-q4_0
30b, 30b-q4_0

模型来源

Ollama 上的 Wizard Vicuna Uncensored 源代码

7b 参数原始来源：Eric Hartford

13b 参数原始来源：Eric Hartford

30b 参数原始来源：Eric Hartford

Wizard Vicuna Uncensored is a 7B, 13B, and 30B parameter model based on Llama 2 uncensored by Eric Hartford. The models were trained against LLaMA-7B with a subset of the dataset, responses that contained alignment / moralizing were removed.

## Get started with Wizard Vicuna Uncensored

The model used in the example below is the Wizard Vicuna Uncensored 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": "wizard-vicuna-uncensored",
  "prompt":"Who made Rose promise that she would never let go?"
 }'
  ```

### CLI

1. Install Ollama
2. Open the terminal and run `ollama run wizard-vicuna-uncensored`

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 wizard-vicuna-uncensored`

## Memory requirements

- 7b models generally require at least 8GB of RAM
- 13b models generally require at least 16GB of RAM
- 30b models generally require at least 32GB 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 |
| 30b, 30b-q4_0 |

## Model source

**Wizard Vicuna Uncensored source on Ollama**

7b parameters original source:
 [Eric Hartford](https://hugging-face.cn/ehartford/Wizard-Vicuna-7B-Uncensored)

13b parameters original source:
 [Eric Hartford](https://hugging-face.cn/ehartford/Wizard-Vicuna-13B-Uncensored)

30b parameters original source:
 [Eric Hartford](https://hugging-face.cn/ehartford/Wizard-Vicuna-30B-Uncensored)

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