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---
title: LLM Transparency Tool
emoji: 🔬🔬🔬
colorFrom: red
colorTo: yellow
sdk: docker
app_file: app.py
pinned: false
---
<h1>
<img width="500" alt="LLM Transparency Tool" src="https://github.com/facebookresearch/llm-transparency-tool/assets/1367529/4bbf2544-88de-4576-9622-6047a056c5c8">
</h1>
<img width="832" alt="screenshot" src="https://github.com/facebookresearch/llm-transparency-tool/assets/1367529/78f6f9e2-fe76-4ded-bb78-a57f64f4ac3a">
## Key functionality
* Choose your model, choose or add your prompt, run the inference.
* Browse contribution graph.
* Select the token to build the graph from.
* Tune the contribution threshold.
* Select representation of any token after any block.
* For the representation, see its projection to the output vocabulary, see which tokens
were promoted/suppressed but the previous block.
* The following things are clickable:
* Edges. That shows more info about the contributing attention head.
* Heads when an edge is selected. You can see what this head is promoting/suppressing.
* FFN blocks (little squares on the graph).
* Neurons when an FFN block is selected.
## Installation
### Dockerized running
```bash
# From the repository root directory
docker build -t llm_transparency_tool .
docker run --rm -p 7860:7860 llm_transparency_tool
```
### Local Installation
```bash
# download
git clone [email protected]:facebookresearch/llm-transparency-tool.git
cd llm-transparency-tool
# install the necessary packages
conda env create --name llmtt -f env.yaml
# install the `llm_transparency_tool` package
pip install -e .
# now, we need to build the frontend
# don't worry, even `yarn` comes preinstalled by `env.yaml`
cd llm_transparency_tool/components/frontend
yarn install
yarn build
```
### Launch
```bash
streamlit run llm_transparency_tool/server/app.py -- config/local.json
```
## Adding support for your LLM
Initially, the tool allows you to select from just a handful of models. Here are the
options you can try for using your model in the tool, from least to most
effort.
### The model is already supported by TransformerLens
Full list of models is [here](https://github.com/neelnanda-io/TransformerLens/blob/0825c5eb4196e7ad72d28bcf4e615306b3897490/transformer_lens/loading_from_pretrained.py#L18).
In this case, the model can be added to the configuration json file.
### Tuned version of a model supported by TransformerLens
Add the official name of the model to the config along with the location to read the
weights from.
### The model is not supported by TransformerLens
In this case the UI wouldn't know how to create proper hooks for the model. You'd need
to implement your version of [TransparentLlm](./llm_transparency_tool/models/transparent_llm.py#L28) class and alter the
Streamlit app to use your implementation.
## License
This code is made available under a [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) license, as found in the LICENSE file.
However you may have other legal obligations that govern your use of other content, such as the terms of service for third-party models.
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