about: Used to report issues and request enhancements for llama.cpp
about: Used to report user-related issues with the software
title: "[User] Insert summary of your issue or enhancement.."
title: "[User] I encountered a problem .."
labels: ''
labels: ''
assignees: ''
assignees: ''
@ -18,11 +18,11 @@ Please answer the following questions for yourself before submitting an issue.
# Expected Behavior
# Expected Behavior
Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do.
Please provide a detailed written description of what you were trying to do, and what you expected `lamma.cpp` to do.
# Current Behavior
# Current Behavior
Please provide a detailed written description of what `llama.cpp` did, instead.
Please provide a detailed written description of what `lamma.cpp` did, instead.
# Environment and Context
# Environment and Context
@ -44,6 +44,20 @@ $ make --version
$ g++ --version
$ g++ --version
```
```
# Models
* The LLaMA models are officially distributed by Facebook and will never be provided through this repository. See this [pull request in Facebook's LLaMA repository](https://github.com/facebookresearch/llama/pull/73/files) if you need to obtain access to the model data.
* If your issue is with model conversion please verify the `sha256sum` of each of your `consolidated*.pth` and `ggml-model-XXX.bin` files to confirm that you have the correct model data files before logging an issue. [Latest sha256 sums for your reference](https://github.com/ggerganov/llama.cpp/issues/238).
* If your issue is with model generation quality then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:
* LLaMA:
* [Introducing LLaMA: A foundational, 65-billion-parameter large language model](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/)
* [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971)
* GPT-3
* [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165)
* GPT-3.5 / InstructGPT / ChatGPT:
* [Aligning language models to follow instructions](https://openai.com/research/instruction-following)
* [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155)
# Failure Information (for bugs)
# Failure Information (for bugs)
Please help provide information about the failure if this is a bug. If it is not a bug, please remove the rest of this template.
Please help provide information about the failure if this is a bug. If it is not a bug, please remove the rest of this template.
@ -61,9 +75,8 @@ Please provide detailed steps for reproducing the issue. We are not sitting in f
Please include any relevant log snippets or files. If it works under one configuration but not under another, please provide logs for both configurations and their corresponding outputs so it is easy to see where behavior changes.
Please include any relevant log snippets or files. If it works under one configuration but not under another, please provide logs for both configurations and their corresponding outputs so it is easy to see where behavior changes.
Also, please try to **avoid using screenshots** if at all possible. Instead, copy/paste the console output and use [Github's markdown](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax) to cleanly format your logs for easy readability.
Also, please try to **avoid using screenshots** if at all possible. Instead, copy/paste the console output and use [Github's markdown](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax) to cleanly format your logs for easy readability. e.g.
### Obtaining and verifying the Facebook LLaMA original model and Stanford Alpaca model data
* The LLaMA models are officially distributed by Facebook and will never be provided through this repository. See this [pull request in Facebook's LLaMA repository](https://github.com/facebookresearch/llama/pull/73/files) if you need to obtain access to the model data.
* Please verify the sha256 checksums of all downloaded model files to confirm that you have the correct model data files before creating an issue relating to your model files.
* The following command will verify if you have all possible latest files in your self-installed `./models` subdirectory:
`sha256sum --ignore-missing -c SHA256SUMS` on Linux
or
`shasum -a 256 --ignore-missing -c SHA256SUMS` on macOS
* If your issue is with model generation quality then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:
* LLaMA:
* [Introducing LLaMA: A foundational, 65-billion-parameter large language model](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/)
* [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971)
* GPT-3
* [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165)
* GPT-3.5 / InstructGPT / ChatGPT:
* [Aligning language models to follow instructions](https://openai.com/research/instruction-following)
* [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155)
### Perplexity (Measuring model quality)
You can pass `--perplexity` as a command line option to measure perplexity over the given prompt. For more background,
see https://huggingface.co/docs/transformers/perplexity. However, in general, lower perplexity is better for LLMs.
#### Latest measurements
The latest perplexity scores for the various model sizes and quantizations are being tracked in [discussion #406](https://github.com/ggerganov/llama.cpp/discussions/406). `llama.cpp` is measuring very well
compared to the baseline implementations. Quantization has a small negative impact to quality, but, as you can see, running
13B at q4_0 beats the 7B f16 model by a significant amount.
All measurements are done against wikitext2 test dataset (https://paperswithcode.com/dataset/wikitext-2), with default options (512 length context).
Note that the changing the context length will have a significant impact on perplexity (longer context = better perplexity).
- There are no strict rules for the code style, but try to follow the patterns in the code (indentation, spaces, etc.). Vertical alignment makes things more readable and easier to batch edit
- There are no strict rules for the code style, but try to follow the patterns in the code (indentation, spaces, etc.). Vertical alignment makes things more readable and easier to batch edit
- Clean-up any trailing whitespaces, use 4 spaces indentation, brackets on same line, `void * ptr`, `int & a`
- Clean-up any trailing whitespaces, use 4 spaces indentation, brackets on same line, `void * ptr`, `int & a`
- See [good first issues](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions
- See [good first issues](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions