|
--- |
|
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. |
|
|
