技能檔案

Llm Tuning Patterns

Name: Llm Tuning Patterns
Author: DojoGenesis

Evidence-based LLM parameter configurations for theorem proving, code generation, and reasoning tasks. Use when tuning temperature, top_p, or max_tokens. Trigger phrases: "tune LLM parameters", "best temperature for", "LLM config for reasoning".

DojoGenesis0 星標2026年4月8日

職業
分類: 機器學習

技能內容

Evidence-based patterns for configuring LLM parameters, based on APOLLO and Godel-Prover research.

Pattern

Different tasks require different LLM configurations. Use these evidence-based settings.

Theorem Proving / Formal Reasoning

Based on APOLLO parity analysis:

Parameter	Value	Rationale
max_tokens	4096	Proofs need space for chain-of-thought
temperature	0.6	Higher creativity for tactic exploration
top_p	0.95	Allow diverse proof paths

Proof Plan Prompt

Always request a proof plan before tactics:

Given the theorem to prove:
[theorem statement]

First, write a high-level proof plan explaining your approach.
Then, suggest Lean 4 tactics to implement each step.

相關技能

Llm Tuning Patterns | Skills Pool

Llm Tuning Patterns

Pattern

Theorem Proving / Formal Reasoning

Proof Plan Prompt

Llm Tuning Patterns

Pattern

Theorem Proving / Formal Reasoning

Proof Plan Prompt

Parallel Sampling

Code Generation

Creative / Exploration Tasks

Anti-Patterns

Source Sessions

Continuous Learning V2

Continuous Learning V2

Continuous Learning V2

Continuous Learning

Continuous Learning

Pytorch Patterns

Parameter	Value	Rationale
max_tokens	2048	Sufficient for most functions
temperature	0.2-0.4	Prefer deterministic output

Parameter	Value	Rationale
max_tokens	4096	Space for exploration
temperature	0.8-1.0	Maximum creativity