llama2:13b-chat-q5_K_M

llama2

Llama 2 是一系列基础语言模型，参数规模从 7B 到 70B 不等。

7b 13b 70b

2.9M 下载量更新于 13 个月前

102 个标签

更新于 13 个月前

13 个月前

34471ad29e7c · 9.2GB

[INST] <<SYS>>{{ .System }}<</SYS>> {{ .Prompt }} [/INST]

59B

参数

{ "stop": [ "[INST]", "[/INST]", "<<SYS>>", "<</SYS>>" ] }

91B

许可

# Llama 2 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and f

4.8kB

许可

LLAMA 2 COMMUNITY LICENSE AGREEMENT Llama 2 Version Release Date: July 18, 2023 "Agreement" means

7.0kB

自述文件

Llama 2 由 Meta Platforms, Inc. 发布。此模型使用 2 万亿个 tokens 训练，默认支持 4096 的上下文长度。 Llama 2 Chat 模型在超过 100 万个人工标注上进行了微调，专为聊天而设计。

CLI

打开终端并运行 ollama run llama2

API

使用 curl 的示例

curl -X POST http://127.0.0.1:11434/api/generate -d '{
  "model": "llama2",
  "prompt":"Why is the sky blue?"
 }'

API 文档

内存要求

7b 模型通常至少需要 8GB 内存
13b 模型通常至少需要 16GB 内存
70b 模型通常至少需要 64GB 内存

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

模型变体

Chat 针对聊天/对话用例进行了微调。这些是 Ollama 中的默认设置，适用于在标签页中标注了 -chat 的模型。

示例： ollama run llama2

Pre-trained 未进行聊天微调。这在标签页中标记为 -text。

示例： ollama run llama2:text

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 后的数字表示用于量化的位数（即 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。

参考

Llama 2: 开放基础和微调的聊天模型

Meta 的 Hugging Face 仓库

Llama 2 is released by Meta Platforms, Inc. This model is trained on 2 trillion tokens, and by default supports a context length of 4096. Llama 2 Chat models are fine-tuned on over 1 million human annotations, and are made for chat.

### CLI

Open the terminal and run `ollama run llama2`

### API

Example using curl:

```bash
curl -X POST http://127.0.0.1:11434/api/generate -d '{
  "model": "llama2",
  "prompt":"Why is the sky blue?"
 }'
```

[API documentation](https://github.com/jmorganca/ollama/blob/main/docs/api.md)

## 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

**Chat** is fine-tuned for chat/dialogue use cases. These are the default in Ollama, and for models tagged with -chat in the tags tab.

*Example: `ollama run llama2`*

**Pre-trained** is without the chat fine-tuning. This is tagged as -text in the tags tab.

*Example: `ollama run llama2:text`*

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.

## References
[Llama 2: Open Foundation and Fine-Tuned Chat Models](https://arxiv.org/abs/2307.09288)

[Meta's Hugging Face repo](http://hugging-face.cn/meta-llama)

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