LLM Selection

Owned by David Prior
Last updated: Apr 13, 2024 by Vladimir Makarov
4 min read

This is the subpage for the LLM Selection sub-team: Jon Stevens, Etzard Stolte, Helena Deus; Brian Evarts; Wouter Franke, Matthijs van der Zee;

 

Notes from the January 24th general PM call:

08:40:27 From Brian Evarts (CPT) to Everyone:
Has anyone tried QLORA or other Quantization techniques for fine tuning?
08:42:05 From stevejs to Everyone:
@Brian we had a QLORA fine-tuned llama2 model that we fine-tuned to increase the sequence length. Quality was OK, but we haven’t used it in production because the model was pretty beefy and we need more infra to increase the speed of the model

 

Notes from the January 26th small team call:

 

Notes from February 1st small team call:

 

Notes from February 15th small team call:

  • Recording: Video Conferencing, Web Conferencing, Webinars, Screen Sharing
    Passcode: @6UEvs7^

  • Transcript: Video Conferencing, Web Conferencing, Webinars, Screen Sharing
    Passcode: @6UEvs7^

  • Warning: BioCypher may not be W3C compliant, and needs discussion in the large team before adoption - or consider alternatives - so far this is the most important question.

    • This team cannot make progress until we make the decision about BioCypher

  • Focus on smaller, cheaper models first? Pick a handful of models, at various size points, look up performance on general benchmarks

  • What is the task → that dictates the choice of the benchmarks

  • Verify that BioChatter has benchmarks for writing cypher queries

  • How important is each benchmark? Perhaps create a linear model that combines multiple scores into a single score

  • Helena: This benchmark answers the question “what are the best embeddings” across a variety of tasks: https://huggingface.co/spaces/mteb/leaderboard

  • Convert into a weekly call at the same time on Thursdays for the next six weeks

 

Notes from February 22nd small team call:

 

Notes from March 7th small team call:

 

Notes from March 14th small team call:

  • This call was short and not recorded

  • The remaining items in the LLM comparison table are costs for the Llama models (Brian to look up) and the performance figures on BioCypher (here we are dependent on Sebastian and may have to wait)

  • There is an expectation, based on team members' work experiences on other projects, that fine-tuning of open-source models may be heavily dependent on use case and may not be cost-effective

  • In that case GPT4 would win

 

Notes from March 21st small team call:

 

Notes from March 28th small team call:

 

Notes from the April 11th small team call:

  • This call was not recorded, but slides with extensive notes and a file with code captured from a Jupyter notebook (VM) are available:

  • Vladimir shared observations on LLM behavior in generation of Cypher queries, and on answering questions in English based on structured input, all corroborated by Jon and Brian (and by Rob vis email earlier)

  • The highest risk step is Cypher code generation

  • Agreed to delegate the LLM testing to the BioCypher team, and meanwhile pick two LLMs for POC (GPT4 and Mistral)

  • Officially close this work stream, because we gathered all information we could and now need to learn more by doing - and actually prototype a POC

  • The new team will be composed of the members of Thursday (LLM choice) and Friday (Open Targets and architecture) sub-teams, and will meet on Fridays

  • The matter of Cypher query generation from plain English questions is discussed here: https://pistoiaalliance.atlassian.net/wiki/spaces/LLMIBR/pages/3323756547