Title: Hi HN community, I have been working on benchmarking publicly available LLMs these past couple of weeks. More precisely, I am interested on the finetuning piece since a lot of businesses are starting to entertain the idea of self-hosting LLMs trained on their proprietary data rather than relying on third party APIs.<p>To this point, I am tracking the following 4 pillars of evaluation that businesses are typically look into:
- Performance
- Time to train an LLM
- Cost to train an LLM
- Inference (throughput &#x2F; latency &#x2F; cost per token)<p>For each LLM, my aim is to benchmark them for popular tasks, i.e., classification and summarization. Moreover, I would like to compare them against each other.<p>So far, I have benchmarked Flan-T5-Large, Falcon-7B and RedPajama and have found them to be very efficient in low-data situations, i.e., when there are very few annotated samples. Llama2-7B&#x2F;13B and Writer’s Palmyra are in the pipeline.<p>But there’s so many LLMs out there! In case this work interests you, would be great to join forces.<p>GitHub repo attached — feedback is always welcome :)<p>Happy hacking!
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