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d84f98799b
## Added ### Core framework - src/llm_client.py — Ollama API wrapper for local Docker inference - src/samples.py — Embedded benchmark datasets (GSM8K/MATH/AIME + Chinese) - src/run_all.py — Unified experiment runner ### Phase 1: Baselines - src/baseline/benchmark.py — IO, CoT, CoT-SC, ToT evaluation with Chinese support ### Phase 2: AGoT - src/agot/agot.py — AGoT core algorithm (6 agent types, recursive decomposition) - src/agot/graph_utils.py — DAG graph data structure with cycle detection - src/agot/prompts.py — 6 agent prompt templates (EN + ZH) - src/agot/run.py — AGoT experiment runner ### Graph Analysis - src/graph_analysis/reasoning_graph.py — Graph property computation (cyclicity, diameter, small-world) - src/graph_analysis/visualize.py — Visualization charts ### Documentation - docs/graph_reasoning_principles.md — Comprehensive principles document - docs/next_steps.md — Roadmap for reliable reasoning tool ### Experiment Results - GSM8K/MATH baselines on qwen2.5:32b and qwq:latest - AGoT validation (100% on small sample) - Chinese dataset benchmarks (C-GSM8K, CMATH) - Graph property analysis confirming Topology of Reasoning findings
19 lines
346 B
Plaintext
19 lines
346 B
Plaintext
# s-LLM-project: Graph-Enhanced Reasoning for 32B LLMs
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# Core dependencies
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ollama>=0.6.0
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tqdm>=4.60.0
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numpy>=1.24.0
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# Graph analysis
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networkx>=3.0
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matplotlib>=3.5
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# For dataset loading (optional, can use embedded samples)
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# datasets>=2.14.0
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# For SFT training (Phase 3)
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# torch>=2.0.0
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# transformers>=4.36.0
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# peft>=0.6.0
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# accelerate>=0.24.0
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