SofiaHussain/big_data

# Llama 2

We are unlocking the power of large language models. Our latest version of Llama is now accessible to individuals, creators, researchers and businesses of all sizes so that they can experiment, innovate and scale their ideas responsibly.

This release includes model weights and starting code for pretrained and fine-tuned Llama language models — ranging from 7B to 70B parameters.

This repository is intended as a minimal example to load [Llama 2](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/) models and run inference. For more detailed examples leveraging Hugging Face, see [llama-recipes](https://github.com/facebookresearch/llama-recipes/).

## Updates post-launch

See [UPDATES.md](UPDATES.md). Also for a running list of frequently asked questions, see [here](https://ai.meta.com/llama/faq/).

## Download

⚠️ **7/18: We're aware of people encountering a number of download issues today. Anyone still encountering issues should remove all local files, re-clone the repository, and [request a new download link](https://ai.meta.com/resources/models-and-libraries/llama-downloads/). It's critical to do all of these in case you have local corrupt files.**

In order to download the model weights and tokenizer, please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License.

Once your request is approved, you will receive a signed URL over email. Then run the download.sh script, passing the URL provided when prompted to start the download.

Pre-requisites: Make sure you have `wget` and `md5sum` installed. Then to run the script: `./download.sh`.

Keep in mind that the links expire after 24 hours and a certain amount of downloads. If you start seeing errors such as `403: Forbidden`, you can always re-request a link.

### Access on Hugging Face

We are also providing downloads on [Hugging Face](https://huggingface.co/meta-llama). You must first request a download from the Meta website using the same email address as your Hugging Face account. After doing so, you can request access to any of the models on Hugging Face and within 1-2 days your account will be granted access to all versions.

## Quick Start

You can follow the steps below to quickly get up and running with Llama 2 models. These steps will let you run quick inference locally. For more examples, see the [Llama 2 recipes repository](https://github.com/facebookresearch/llama-recipes).

1. In a conda env with PyTorch / CUDA available clone and download this repository.

2. In the top level directory run:
```bash
pip install -e .
```
3. Visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and register to download the model/s.

4. Once registered, you will get an email with a URL to download the models. You will need this URL when you run the download.sh script.

5. Once you get the email, navigate to your downloaded llama repository and run the download.sh script.
- Make sure to grant execution permissions to the download.sh script
- During this process, you will be prompted to enter the URL from the email.
- Do not use the “Copy Link” option but rather make sure to manually copy the link from the email.

6. Once the model/s you want have been downloaded, you can run the model locally using the command below:
```bash
torchrun --nproc_per_node 1 example_chat_completion.py \
--ckpt_dir llama-2-7b-chat/ \
--tokenizer_path tokenizer.model \
--max_seq_len 512 --max_batch_size 6
```
**Note**
- Replace `llama-2-7b-chat/` with the path to your checkpoint directory and `tokenizer.model` with the path to your tokenizer model.
- The `–nproc_per_node` should be set to the [MP](#inference) value for the model you are using.
- Adjust the `max_seq_len` and `max_batch_size` parameters as needed.
- This example runs the [example_chat_completion.py](example_chat_completion.py) found in this repository but you can change that to a different .py file.

## Inference

Different models require different model-parallel (MP) values:

| Model | MP |
|--------|----|
| 7B | 1 |
| 13B | 2 |
| 70B | 8 |

All models support sequence length up to 4096 tokens, but we pre-allocate the cache according to `max_seq_len` and `max_batch_size` values. So set those according to your hardware.

### Pretrained Models

These models are not finetuned for chat or Q&A. They should be prompted so that the expected answer is the natural continuation of the prompt.

See `example_text_completion.py` for some examples. To illustrate, see the command below to run it with the llama-2-7b model (`nproc_per_node` needs to be set to the `MP` value):

```
torchrun --nproc_per_node 1 example_text_completion.py \
--ckpt_dir llama-2-7b/ \
--tokenizer_path tokenizer.model \
--max_seq_len 128 --max_batch_size 4
```

### Fine-tuned Chat Models

The fine-tuned models were trained for dialogue applications. To get the expected features and performance for them, a specific formatting defined in [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212)
needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces).

You can also deploy additional classifiers for filtering out inputs and outputs that are deemed unsafe. See the llama-recipes repo for [an example](https://github.com/facebookresearch/llama-recipes/blob/main/inference/inference.py) of how to add a safety checker to the inputs and outputs of your inference code.

Examples using llama-2-7b-chat:

```
torchrun --nproc_per_node 1 example_chat_completion.py \
--ckpt_dir llama-2-7b-chat/ \
--tokenizer_path tokenizer.model \
--max_seq_len 512 --max_batch_size 6
```

Llama 2 is a new technology that carries potential risks with use. Testing conducted to date has not — and could not — cover all scenarios.
In order to help developers address these risks, we have created the [Responsible Use Guide](Responsible-Use-Guide.pdf). More details can be found in our research paper as well.

## Issues

Please report any software “bug”, or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)

## Model Card
See [MODEL_CARD.md](MODEL_CARD.md).

## License

Our model and weights are licensed for both researchers and commercial entities, upholding the principles of openness. Our mission is to empower individuals, and industry through this opportunity, while fostering an environment of discovery and ethical AI advancements.

See the [LICENSE](LICENSE) file, as well as our accompanying [Acceptable Use Policy](USE_POLICY.md)

## References

1. [Research Paper](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/)
2. [Llama 2 technical overview](https://ai.meta.com/resources/models-and-libraries/llama)
3. [Open Innovation AI Research Community](https://ai.meta.com/llama/open-innovation-ai-research-community/)

For common questions, the FAQ can be found [here](https://ai.meta.com/llama/faq/) which will be kept up to date over time as new questions arise.

## Original LLaMA
The repo for the original llama release is in the [`llama_v1`](https://github.com/facebookresearch/llama/tree/llama_v1) branch.

原始信息汇总

Llama 2 数据集概述

数据集简介

Llama 2 是一个大型语言模型，包含从 7B 到 70B 参数的预训练和微调模型。该数据集旨在帮助个人、创作者、研究人员和企业负责任地实验、创新和扩展他们的想法。

数据集内容

• 模型权重和代码：包括预训练和微调的 Llama 语言模型。
• 示例代码：提供最小示例以加载和运行 Llama 2 模型进行推理。

下载与访问

• 下载流程：访问 Meta 网站并接受许可协议后，通过电子邮件接收签名 URL，运行 download.sh 脚本进行下载。
• Hugging Face 访问：也可通过 Hugging Face 下载，需使用与 Hugging Face 账户相同的电子邮件地址在 Meta 网站请求下载。

快速开始

• 环境设置：在包含 PyTorch/CUDA 的 conda 环境中克隆并下载仓库。
• 安装依赖：运行 pip install -e . 安装必要的包。
• 模型下载：注册后通过电子邮件获取下载链接，运行 download.sh 脚本。
• 本地运行：使用提供的命令在本地运行模型进行推理。

推理配置

• 模型并行值 (MP)：不同模型需要不同的 MP 值。

  模型	MP
  7B	1
  13B	2
  70B	8

• 序列长度和批量大小：根据硬件调整 max_seq_len 和 max_batch_size 参数。

模型类型

• 预训练模型：未针对聊天或问答进行微调，适用于自然语言生成。
• 微调聊天模型：针对对话应用进行微调，需要特定的格式和标记。

安全与责任

• 风险提示：Llama 2 是一项新技术，使用时存在潜在风险。
• 责任使用指南：提供《责任使用指南》以帮助开发者应对这些风险。

问题报告

• 模型问题：通过 GitHub 仓库报告模型问题。
• 风险内容：通过指定链接报告模型生成的风险内容。
• 安全问题：通过指定链接报告安全问题。

许可证

• 开放许可：模型和权重对研究人员和商业实体开放，遵循开放原则和伦理 AI 发展。

参考资料

• 研究论文：提供详细的技术和研究信息。
• 技术概览：提供模型的技术概览。
• 开放创新 AI 研究社区：参与开放创新 AI 研究社区。