base on Steer LLM outputs towards a certain topic/subject and enhance response capabilities using activation engineering by adding steering vectors # LLM Steer A Python module to steer LLM responses towards a certain topic/subject and to enhance capabilities (e.g., making it provide correct responses to tricky logical puzzles more often). A practical tool for using activation engineering by adding steer vectors to different layers of a Large Language Model (LLM). It should be used along with the transformers library. ## Demo Google Colab demo: https://colab.research.google.com/github/Mihaiii/llm_steer/blob/main/demo/llm_steer_demo.ipynb ## Basic usage Install it: `pip install llm_steer` Then use: ```python from llm_steer import Steer steered_model = Steer(model, tokenizer) ``` Add a steering vector on a particular layer of the model with a given coefficient and text. The coefficient can also be negative. ```python steered_model.add(layer_idx=20, coeff=0.4, text="logical") ``` Get all the applied steering vectors: ```python steered_model.get_all() ``` Remove all steering vectors to revert to initial model: ```python steered_model.reset_all() ``` ## Advanced usage The so-called "advanced usage" involves changing the default values of 2 parameters ("try_keep_nr" and "exclude_bos_token"), which, from my experiments - almost always leads to the LLM outputting gibberish. In the very few cases when the LLM outputs text that does make sense, **the basic usage provides higher quality outputs**. More info will be provided in a separate file. ## Q / A Q: What's the difference between llm_steer and mentioning what you want in the system prompt? A: I see llm_steer as an enhancer. It can be used together with the system prompt. <br/> Q: How to determine the best parameters to be used? A: I don't have a method; it's all trial and error. I recommend starting with a small coefficient and then slowly increase it. <br/> Q: What models are supported? A: I tested it on multiple architectures, including LLaMa, Mistral, Phi, StableLM. Keep in mind that llm_steer is meant to be used together with HuggingFace's transformers library, so it won't work on GGUF, for example. <br/> Q: I applied steering vectors, but the LLM outputs gibberish. What should I do? A: Try a lower coeff value or another layer. <br/> Q: Can I add multiple steering vectors on the same layer? Can I add the same steering vector on multiple layers? Can I add steering vectors with negative coefficients? A: Yes, and please do. llm_steer is built for experimenting. See the Colab for examples: https://colab.research.google.com/github/Mihaiii/llm_steer/blob/main/demo/llm_steer_demo.ipynb <br/> Q: Can I use steer vectors to enhance role-play characteristics (e.g., personas that are more funny or cocky)? A: I believe this is possible, but I haven't had good results yet. I'm considering doing some more intensive testing and I might write a new notebook for it. <br/> Q: Can I use negative steering vectors to force it not to say "As an AI language model"? A: Yes. ## Credits / Thanks - [DL Explorers](https://www.youtube.com/@DLExplorers-lg7dt) for his video on [activation engineer](https://www.youtube.com/watch?v=J2Gx6FFEaRY&t=29s) which goes over [an article](https://www.greaterwrong.com/posts/5spBue2z2tw4JuDCx/steering-gpt-2-xl-by-adding-an-activation-vector) and [a colab he made](https://colab.research.google.com/github/githubpradeep/notebooks/blob/main/activation_engineering.ipynb). The resources mentioned in that video were the starting point of llm_steer. - Gary Bernhardt for his excellent [Python for programmers](https://www.executeprogram.com/courses/python-for-programmers) course. I needed a course that could help me go through the basics of Python without treating me like a dev noob (like most basic level tutorials treat their audience). - Andrej Karpathy for his [State of GPT](https://www.youtube.com/watch?v=bZQun8Y4L2A) video. I always wanted to make an open-source project, but there already was a repo for every idea I had. Not when it comes to tools for LLMs, though! ", Assign "at most 3 tags" to the expected json: {"id":"7286","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"