From dbbd3fdc1d0f6f4b806332a4990dacd72b7d1353 Mon Sep 17 00:00:00 2001 From: souzatharsis Date: Tue, 10 Dec 2024 14:39:31 -0300 Subject: [PATCH] update promptfoo evals --- .../_build/.doctrees/environment.pickle | Bin 2718048 -> 2736624 bytes .../_build/.doctrees/notebooks/evals.doctree | Bin 835003 -> 842682 bytes .../html/_sources/notebooks/evals.ipynb | 19 ++++++++ tamingllms/_build/html/notebooks/evals.html | 41 +++++++++++++++++- tamingllms/_build/html/objects.inv | Bin 1128 -> 1151 bytes tamingllms/_build/html/searchindex.js | 2 +- .../jupyter_execute/markdown/intro.ipynb | 2 +- .../jupyter_execute/notebooks/evals.ipynb | 19 ++++++++ tamingllms/notebooks/evals.ipynb | 19 ++++++++ 9 files changed, 98 insertions(+), 4 deletions(-) diff --git a/tamingllms/_build/.doctrees/environment.pickle b/tamingllms/_build/.doctrees/environment.pickle index 42cf12da5b3182d23719a7750115b13e9b1ac25e..095cf8d5ebe89c7e72d478f9e580af406f2fcdcc 100644 GIT binary patch delta 294078 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  • LangSmith

  • PromptFoo

    • +

  • Comparison

    • -

  • References

    • +

  • References

    • @@ -2205,9 +2206,45 @@

    Prompt Comparison R

    The results show that prompt3.txt performs best for Legal Proceedings sections, achieving a perfect score of 1.0 compared to 0.5 for prompt2.txt and 0.1 for prompt1.txt. For Risk Factors sections, both prompt2.txt and prompt3.txt achieve moderate scores of 0.5, while prompt1.txt scores poorly at 0.1. This suggests that prompt3.txt is generally more effective at extracting detailed information, particularly for legal content. In summary, defining a Role and a requirement for the output to be detailed is a good way to improve the quality of the summaries at least for this specific task, model and criteria.

    In conclusion, Promptfoo can serve as an effective LLM application evaluation tool particularly for its ability to decouple several components of the evaluation process. Hence enabling the user to focus on the most important aspects of the evaluation given the particular application and criteria making it a valuable and flexible tool for LLM application development.

    +
    +

    4.8.4. Comparison¶

    +

    The following table provides a summarized comparative analysis of three open source frameworks for language models evaluation we have discussed: Lighteval, LangSmith, and Promptfoo. Each framework is assessed based on key features such as integration capabilities, customization options, ease of use, and the ability to facilitate human and LLM collaboration.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Table 4.6 Comparison of Lighteval, LangSmith, and Promptfoo¶

    Feature/Aspect

    Lighteval

    LangSmith

    Promptfoo

    Integration

    Seamless with Hugging Face models, easy access to multiple inference engines, and remote evaluation (e.g., TGI servers, HF serverless models)

    User-provided models, evaluators, and metrics

    CLI-based, user-provided models via YAML

    Customization

    Flexible task and metric support, quick evaluation against state-of-the-art leaderboards

    Easy setup of custom tasks and metrics with plain vanilla Python functions, lacks predefined tasks and metrics

    Default and user-provided probes, metrics, and assertions

    Ease of Use

    User-friendly, minimal setup

    User-friendly, minimal setup, includes UI for result visualization

    Simple CLI, rapid testing, includes UI for result visualization

    Human/LLM Collaboration

    Model-based evaluation

    Model-based evaluation

    Supports human and model evaluators

    +
    -

    4.9. References¶

    +

    4.9. References¶

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3, "usag": 3, "discuss": [3, 4], "implic": 3, "futur": 3, "conclus": [3, 4], "user": 4, "need": 4, "solut": 4, "strategi": 4, "techniqu": 4, "One": 4, "shot": 4, "specif": 4, "json": 4, "mode": 4, "langchain": 4, "outlin": 4, "ollama": 4, "compar": 4, "framework": 4, "best": 4, "research": 4, "ongo": 4, "debat": 4, "acknowledg": 4}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 8, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1, "sphinxcontrib.bibtex": 9, "sphinx": 57}, "alltitles": {"Introduction": [[0, "introduction"], [4, "introduction"]], "Contents": [[0, "contents"], [2, "contents"], [3, "contents"], [4, "contents"]], "Core Challenges We\u2019ll Address": [[0, "core-challenges-we-ll-address"]], "A Practical Approach": [[0, "a-practical-approach"]], "A Note on Perspective": [[0, "a-note-on-perspective"]], "Who This Book Is For": [[0, "who-this-book-is-for"]], "Outcomes": [[0, "outcomes"]], "Prerequisites": [[0, "prerequisites"]], "Setting Up Your Environment": [[0, "setting-up-your-environment"]], "Python Environment Setup": [[0, "python-environment-setup"]], "API Keys Configuration": [[0, "api-keys-configuration"]], "Code Repository": [[0, "code-repository"]], "Troubleshooting Common Issues": [[0, "troubleshooting-common-issues"]], "About the Author(s)": [[0, "about-the-author-s"]], "Taming LLMs": [[1, "taming-llms"]], "A Practical Guide to LLM Pitfalls with Open Source Software": [[1, "a-practical-guide-to-llm-pitfalls-with-open-source-software"]], "Chapter 1: Introduction": [[1, "chapter-1-introduction"]], "Chapter 2: Wrestling with Structured Output": [[1, "chapter-2-wrestling-with-structured-output"]], "Chapter 3: Input Size and Length Limitations": [[1, "chapter-3-input-size-and-length-limitations"]], "Chapter 4: Output Size and Length Limitations": [[1, "chapter-4-output-size-and-length-limitations"]], "Chapter 5: The Evals Gap": [[1, "chapter-5-the-evals-gap"]], "Chapter 6: Hallucination: The Reality Gap": [[1, "chapter-6-hallucination-the-reality-gap"]], "Chapter 7: Safety Concerns": [[1, "chapter-7-safety-concerns"]], "Chapter 8: The Cost Factor": [[1, "chapter-8-the-cost-factor"]], "Chapter 9: Breaking Free from Cloud Providers": [[1, "chapter-9-breaking-free-from-cloud-providers"]], "Appendix A: Tools and Resources": [[1, "appendix-a-tools-and-resources"]], "Citation": [[1, "citation"]], "The Evals Gap": [[2, "the-evals-gap"]], "Non-Deterministic Generative Machines": [[2, "non-deterministic-generative-machines"]], "Temperature and Sampling": [[2, "temperature-and-sampling"]], "The Temperature Spectrum": [[2, "the-temperature-spectrum"]], "Emerging Properties": [[2, "emerging-properties"]], "Problem Statement": [[2, "problem-statement"], [3, "problem-statement"], [4, "problem-statement"]], "Evals of Traditional Software vs LLMs": [[2, "evals-table"]], "Evals Design": [[2, "evals-design"]], "LLM Application Testing Requirements Matrix": [[2, "validation-requirements"]], "Conceptual Overview": [[2, "conceptual-overview"]], "Design Considerations": [[2, "design-considerations"]], "Metrics": [[2, "metrics"]], "Key Metrics for Evaluating Generative Tasks": [[2, "key-metrics"]], "Evaluators": [[2, "evaluators"]], "Model-Based Evaluation": [[2, "model-based-evaluation"]], "Human-Based Evaluation": [[2, "human-based-evaluation"]], "Evaluating Evaluators": [[2, "evaluating-evaluators"]], "Benchmarks and Leaderboards": [[2, "benchmarks-and-leaderboards"]], "Tools": [[2, "tools"]], "LightEval": [[2, "lighteval"]], "MMLU Econometrics Task Dataset sample": [[2, "mmlu-econometrics"]], "Model Families Evaluated Using LightEval": [[2, "model-families"]], "LangSmith": [[2, "langsmith"]], "PromptFoo": [[2, "promptfoo"]], "Comparison": [[2, "comparison"]], "Comparison of Lighteval, LangSmith, and Promptfoo": [[2, "tool-comparison"]], "References": [[2, "references"], [3, "references"], [4, "references"]], "Output Size Limitations": [[3, "output-size-limitations"]], "What are Token Limits?": [[3, "what-are-token-limits"]], "Token Cost and Length Limitation Comparison Across Key Models": [[3, "token-cost-table"]], "Content Chunking with Contextual Linking": [[3, "content-chunking-with-contextual-linking"]], "Generating long-form content": [[3, "generating-long-form-content"]], "Step 1: Chunking the Content": [[3, "step-1-chunking-the-content"]], "Step 2: Writing the Base Prompt Template": [[3, "step-2-writing-the-base-prompt-template"]], "Step 3: Constructing Dynamic Prompt Parameters": [[3, "step-3-constructing-dynamic-prompt-parameters"]], "Step 4: Generating the Report": [[3, "step-4-generating-the-report"]], "Example Usage": [[3, "example-usage"]], "Discussion": [[3, "discussion"], [4, "discussion"]], "Implications": [[3, "implications"]], "Future Considerations": [[3, "future-considerations"]], "Conclusion": [[3, "conclusion"], [4, "conclusion"]], "Wrestling with Structured Output": [[4, "wrestling-with-structured-output"]], "User Needs": [[4, "user-needs"]], "Solutions": [[4, "solutions"]], "Strategies": [[4, "strategies"]], "Techniques and Tools": [[4, "techniques-and-tools"]], "One-Shot Prompts": [[4, "one-shot-prompts"]], "Structured Output with Provider-Specific APIs": [[4, "structured-output-with-provider-specific-apis"]], "JSON Mode": [[4, "json-mode"]], "LangChain": [[4, "langchain"]], "Outlines": [[4, "outlines"]], "Ollama": [[4, "ollama"]], "Comparing Solutions": [[4, "comparing-solutions"]], "Structured Output Frameworks Comparison": [[4, "structured-output-frameworks"]], "Best Practices": [[4, "best-practices"]], "Research and Ongoing Debate": [[4, "research-and-ongoing-debate"]], "Acknowledgements": [[4, "acknowledgements"]]}, "indexentries": {}}) \ No newline at end of file diff --git a/tamingllms/_build/jupyter_execute/markdown/intro.ipynb b/tamingllms/_build/jupyter_execute/markdown/intro.ipynb index f80237a..02830f8 100644 --- a/tamingllms/_build/jupyter_execute/markdown/intro.ipynb +++ b/tamingllms/_build/jupyter_execute/markdown/intro.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "60c9196f", + "id": "3693a9ca", "metadata": {}, "source": [ "(intro)=\n", diff --git a/tamingllms/_build/jupyter_execute/notebooks/evals.ipynb b/tamingllms/_build/jupyter_execute/notebooks/evals.ipynb index 860c760..9419738 100644 --- a/tamingllms/_build/jupyter_execute/notebooks/evals.ipynb +++ b/tamingllms/_build/jupyter_execute/notebooks/evals.ipynb @@ -2511,6 +2511,25 @@ "In conclusion, Promptfoo can serve as an effective LLM application evaluation tool particularly for its ability to decouple several components of the evaluation process. Hence enabling the user to focus on the most important aspects of the evaluation given the particular application and criteria making it a valuable and flexible tool for LLM application development." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparison\n", + "\n", + "The following table provides a summarized comparative analysis of three open source frameworks for language models evaluation we have discussed: Lighteval, LangSmith, and Promptfoo. Each framework is assessed based on key features such as integration capabilities, customization options, ease of use, and the ability to facilitate human and LLM collaboration.\n", + "\n", + "```{table} Comparison of Lighteval, LangSmith, and Promptfoo\n", + ":name: tool-comparison\n", + "| Feature/Aspect | Lighteval | LangSmith | Promptfoo |\n", + "|----------------------|------------------------------------|------------------------------------|------------------------------------|\n", + "| **Integration** | Seamless with Hugging Face models, easy access to multiple inference engines, and remote evaluation (e.g., TGI servers, HF serverless models) | User-provided models, evaluators, and metrics | CLI-based, user-provided models via YAML |\n", + "| **Customization** | Flexible task and metric support, quick evaluation against state-of-the-art leaderboards | Easy setup of custom tasks and metrics with plain vanilla Python functions, lacks predefined tasks and metrics | Default and user-provided probes, metrics, and assertions |\n", + "| **Ease of Use** | User-friendly, minimal setup | User-friendly, minimal setup, includes UI for result visualization | Simple CLI, rapid testing, includes UI for result visualization |\n", + "| **Human/LLM Collaboration** | Model-based evaluation | Model-based evaluation | Supports human and model evaluators |\n", + "```" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/tamingllms/notebooks/evals.ipynb b/tamingllms/notebooks/evals.ipynb index 75041e9..3eeebfa 100644 --- a/tamingllms/notebooks/evals.ipynb +++ b/tamingllms/notebooks/evals.ipynb @@ -2511,6 +2511,25 @@ "In conclusion, Promptfoo can serve as an effective LLM application evaluation tool particularly for its ability to decouple several components of the evaluation process. Hence enabling the user to focus on the most important aspects of the evaluation given the particular application and criteria making it a valuable and flexible tool for LLM application development." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparison\n", + "\n", + "The following table provides a summarized comparative analysis of three open source frameworks for language models evaluation we have discussed: Lighteval, LangSmith, and Promptfoo. Each framework is assessed based on key features such as integration capabilities, customization options, ease of use, and the ability to facilitate human and LLM collaboration.\n", + "\n", + "```{table} Comparison of Lighteval, LangSmith, and Promptfoo\n", + ":name: tool-comparison\n", + "| Feature/Aspect | Lighteval | LangSmith | Promptfoo |\n", + "|----------------------|------------------------------------|------------------------------------|------------------------------------|\n", + "| **Integration** | Seamless with Hugging Face models, easy access to multiple inference engines, and remote evaluation (e.g., TGI servers, HF serverless models) | User-provided models, evaluators, and metrics | CLI-based, user-provided models via YAML |\n", + "| **Customization** | Flexible task and metric support, quick evaluation against state-of-the-art leaderboards | Easy setup of custom tasks and metrics with plain vanilla Python functions, lacks predefined tasks and metrics | Default and user-provided probes, metrics, and assertions |\n", + "| **Ease of Use** | User-friendly, minimal setup | User-friendly, minimal setup, includes UI for result visualization | Simple CLI, rapid testing, includes UI for result visualization |\n", + "| **Human/LLM Collaboration** | Model-based evaluation | Model-based evaluation | Supports human and model evaluators |\n", + "```" + ] + }, { "cell_type": "markdown", "metadata": {},

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