llama2:7b-chat-q3_K_L

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 标签

更新于 13 个月前

13 个月前

c04042faafd0 · 3.6GB

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

59B

参数

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

91B

许可证

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

4.8kB

许可证

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

7.0kB

说明

Llama 2 由 Meta Platforms, Inc. 发布。此模型使用 2 万亿 tokens 训练，默认支持 4096 的上下文长度。Llama 2 Chat 模型在超过 100 万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
70b 模型通常至少需要 64GB 的 RAM

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 中的默认模型，并且对于在标签页中标注了 -chat 标签的模型也是默认模型。

示例: ollama run llama2

Pre-trained 版本没有聊天微调。这在标签页中标记为 -text 标签。

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 后面的数字表示用于量化的位数（即 q4 表示 4 位量化）。数字越高，模型越准确，但运行速度越慢，并且需要的内存越多。

参考

Llama 2: Open Foundation and Fine-Tuned Chat Models

Meta’s Hugging Face repo

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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