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

安裝 Ollama
開啟終端機並執行 ollama run wizard-vicuna-uncensored

注意：如果模型尚未下載，ollama run 命令會執行 ollama pull。若要在不執行的情況下下載模型，請使用 ollama pull wizard-vicuna-uncensored

記憶體需求

7b 模型通常需要至少 8GB 的 RAM
13b 模型通常需要至少 16GB 的 RAM
30b 模型通常需要至少 32GB 的 RAM

如果您在使用較高的量化級別時遇到問題，請嘗試使用 q4 模型或關閉任何其他佔用大量記憶體的程式。

模型變體

預設情況下，Ollama 使用 4 位元量化。若要嘗試其他量化級別，請嘗試其他標籤。q 後面的數字代表用於量化的位元數（即 q4 表示 4 位元量化）。數字越高，模型越準確，但執行速度越慢，且需要的記憶體越多。

別名
latest, 7b, 7b-q4_0
13b, 13b-q4_0
30b, 30b-q4_0

模型來源

Ollama 上的 Wizard Vicuna Uncensored 來源

7b 參數原始來源：Eric Hartford

13b 參數原始來源：Eric Hartford

30b 參數原始來源：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://huggingface.co/ehartford/Wizard-Vicuna-7B-Uncensored)

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

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

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