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kagisearch/pyllms

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Minimal Python library to connect to LLMs (OpenAI, Anthropic, Google, Groq, Reka, Together, AI21, Cohere, Aleph Alpha, HuggingfaceHub), with a built-in model performance benchmark.

11 mentions on Hacker News by 5 people: 2 stories, 9 comments · first 2023-04-11 · latest 2024-09-05

Mentions per year 11 mentions · busiest year 2023 (7)
20082026
  1. …library to connect to various Language Models (LLMs) with a built-in model performance benchmark. https://github.com/kagisearch/pyllms
    comment by selcuka · 2024-09-05 · in Kagi Assistant
  2. https://github.com/kagisearch/pyllms/blob/70ec2a4fa4a233ee80...
    comment by SushiHippie · 2024-07-31 · in Kagi LLM Benchmarking Project
  3. …OpenAIProvider('gpt-4o') Median Latency: 3.25 | Aggregated speed: 53.75 | Accuracy: 74.07% | [1] https://github.com/kagisearch/pyllms
    comment by freediver · 2024-07-24 · in Large Enough
  4. …Total Cost: 0.00735 | Aggregated speed: 117.53 tok/sec | Accuracy: 48.15% [1] https://github.com/kagisearch/pyllms
  5. …juice was too sweet. Wonder if the synthetic benchmarks are very biased and not that useful. [1] https://github.com/kagisearch/pyllms/blob/6c11cd51b099d33e61...
    comment by freediver · 2023-07-06 · in InternLM – new open source 7B LLM
  6. For anyone reading this, these are the actual prompts being used to assess the models. https://github.com/kagisearch/pyllms/blob/ca9ad4d4bfdd9d58fe...
  7. Less scientific, but arguably more practical benchmarks here: https://github.com/kagisearch/pyllms#model-benchmarks
  8. …Anthropic for this demo, big fans. The entire demo is 20 lines of code, and uses PyLLMs [1] [1] https://github.com/kagisearch/pyllms
  9. ▲ 6
    Show HN: PyLLMs: – Connect and compare top AI models in Python
    story by freediver · 2023-04-13 · 1 comment
  10. ▲ 2
    Lightweight Python library to connect to large language models
    story by caprock · 2023-04-11 · 0 comments
  11. …(openai, anthropic, AI21..) and as a part of that we designed a LLM benchmark. All open source. [1] https://github.com/kagisearch/pyllms/tree/main#benchmarks