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