Show HN: Klarity – OS tool to debug LLM reasoning patterns with entropy analysis

3 points

a year ago

After struggling to understand why our reasoning models would sometimes produce flawless reasoning or go completely off track - we updated Klarity to get instant insights into reasoning uncertainty and concrete suggestions for dataset and prompt optimization. Just point it at your model to save testing time.

Key new features:

- Identify where your model's reasoning goes off track with step-by-step entropy analysis - Get actionable scores for coherence and confidence at each reasoning step - Training data insights: Identify which reasoning data lead to high-quality outputs

Structured JSON output with step-by-step analysis:

- steps: array of {step_number, content, entropy_score, semantic_score, top_tokens[]} - quality_metrics: array of {step, coherence, relevance, confidence} - reasoning_insights: array of {step, type, pattern, suggestions[]} - training_targets: array of {aspect, current_issue, improvement}

Example use cases:

- Debug why your model's reasoning edge cases - Identify which types of reasoning steps contribute to better outcomes - Optimize your RL datasets by focusing on high-quality reasoning patterns

Currently supports Hugging Face transformers and Together AI API, we tested the library with DeepSeek R1 distilled series (Qwen-1.5b, Qwen-7b etc)

Installation: `pip install git+https://github.com/klara-research/klarity.git`

We are building OS interpretability/explainability tools to debug generative models behaviors. What insights would actually help you debug these black box systems?

Links:

- Repo: https://github.com/klara-research/klarity - Our website: [https://klaralabs.com](https://klaralabs.com/) - Discord: https://discord.gg/wCnTRzBE

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