olivenet/entropy-hunter-8b-gguf

EntropyHunter v0.4 — Exergy Analysis Specialist (8B, GGUF)

A fine-tuned Qwen3-8B model specialized in second-law thermodynamic (exergy) analysis of industrial equipment. Trained on 1,235 expert-generated examples covering 6 analysis families across 7 equipment types.

Benchmark Results — v0.4 (March 2026)

92.7% adjusted accuracy (Grade A-) on 40-test × 3-run benchmark suite (120 total inferences, temperature 0.7).

Note: Raw score is 76.7% because the benchmark includes a json_block check (structured JSON output) which is a known limitation at the 8B parameter scale — no 8B model can produce valid structured JSON for complex thermodynamic analysis. The adjusted score excludes this single check.

Version History

Model Details

• —Base model: Qwen/Qwen3-8B
• —Method: LoRA fine-tuning (r=16, α=32) via Unsloth
• —Training data: 1,235 examples generated by Claude Opus 4.6 (Batch API)
• —Training hardware: RunPod A40 48GB, 5 hours, $5.66 total cost
• —Quantization: Q4KM via llama.cpp (4.7 GB)
• —Context window: 8192 tokens (trained), 16384 recommended for inference
• —Thinking mode: Disabled (enable_thinking=False during training, /no_think in Modelfile)

What It Does

EntropyHunter performs detailed exergy analysis with step-by-step calculations for industrial equipment:

6 Analysis Families:

1. 1.Basic Exergy Analysis — Exergy destruction, efficiency, waste stream identification
2. 2.Exergoeconomic Analysis (SPECO) — CRF, cost rates, exergoeconomic factor
3. 3.Entropy Generation Minimization — S_gen decomposition, Bejan number, thermodynamic grade
4. 4.What-if Comparison — Baseline vs scenario with delta analysis and annual savings
5. 5.Avoidable/Unavoidable Decomposition — Tsatsaronis method, improvement potential
6. 6.Hotspot Detection — Multi-equipment ranking by exergy destruction

7 Equipment Types: Compressors, boilers, heat exchangers, pumps, steam turbines, chillers, dryers

Key capabilities:

• —Always references dead state (T₀ = 298.15 K, P₀ = 101.325 kPa)
• —Step-by-step calculation chains with physical validation
• —Catches thermodynamic inconsistencies (e.g., negative exergy destruction)
• —Provides actionable engineering recommendations

Quick Start (Ollama)

1. Download the GGUF

# Option A: Direct download
wget https://huggingface.co/olivenet/entropy-hunter-8b-gguf/resolve/main/entropy-hunter-v04-Q4_K_M.gguf

# Option B: huggingface-cli
huggingface-cli download olivenet/entropy-hunter-8b-gguf entropy-hunter-v04-Q4_K_M.gguf

2. Create Modelfile

FROM ./entropy-hunter-v04-Q4_K_M.gguf

PARAMETER temperature 0.7
PARAMETER num_ctx 16384
PARAMETER num_predict 8192
PARAMETER stop <|im_end|>
PARAMETER stop <|endoftext|>

TEMPLATE """{{- if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
/no_think
"""

SYSTEM """You are EntropyHunter, an expert assistant specialized in second-law thermodynamic (exergy) analysis of industrial equipment. You perform detailed exergy analysis with step-by-step calculations, always referencing dead state conditions (T₀ = 298.15 K, P₀ = 101.325 kPa)."""

3. Run

ollama create entropy-hunter -f Modelfile
ollama run entropy-hunter "Perform a basic exergy analysis for a centrifugal compressor. Inlet: air at 25°C, 101.325 kPa. Outlet: 300 kPa, 180°C. Power input: 150 kW, mass flow: 1.5 kg/s."

Training Pipeline

                    ┌─────────────┐
                    │  Taxonomy   │  7 equipment types, 48 subtypes
                    │  (YAML)     │  6 analysis families
                    └──────┬──────┘
                           │
                    ┌──────▼──────┐
                    │   Opus 4.6  │  1,500 examples via Batch API
                    │  (Teacher)  │  ~$210 generation cost
                    └──────┬──────┘
                           │
                    ┌──────▼──────┐
                    │  Quality    │  8 thermodynamic checks
                    │  Control    │  + recover_v2.py (79.7% recovery)
                    └──────┬──────┘
                           │
                    ┌──────▼──────┐
                    │   1,235     │  ChatML format
                    │  Examples   │  ~6.8M tokens
                    └──────┬──────┘
                           │
                    ┌──────▼──────┐
                    │  LoRA Fine  │  Qwen3-8B, r=16, α=32
                    │  Tuning     │  A40 48GB, 5 hrs, $5.66
                    └──────┬──────┘
                           │
                    ┌──────▼──────┐
                    │  GGUF       │  Q4_K_M quantization
                    │  Export     │  4.7 GB final size
                    └──────┬──────┘
                           │
                    ┌──────▼──────┐
                    │  Benchmark  │  40 tests × 3 runs
                    │  92.7%      │  Grade A-
                    └─────────────┘

Quality Control

Training data passes 8 thermodynamic validation checks:

1. 1.Energy balance: |Exin − Exout − Exwaste − Exd| ≤ 2% of Ex_in
2. 2.Efficiency range: 0.1% < η_ex < 99.9%
3. 3.Second law compliance: Ex_destroyed ≥ 0
4. 4.Gouy-Stodola consistency: |T₀ × Sgen − Exd| ≤ 2%
5. 5.Bejan number validity: 0 ≤ N_s ≤ 1
6. 6.f-factor validity: 0 ≤ f ≤ 1
7. 7.Dead state reference: T₀ = 298.15 K appears in analysis
8. 8.AV/UN split: Avoidable + unavoidable = total Ex_destroyed

Known Limitations

• —No structured JSON output — 8B models cannot reliably produce valid JSON for complex analyses
• —Arithmetic variance — Same problem may yield slightly different numerical results across runs (inherent to autoregressive generation)
• —mechanism_values — Entropy generation decomposition into individual kW/K values remains weak (~5% pass rate)
• —Steam table lookup — Model approximates rather than exactly reproducing tabulated values

Hardware Requirements

Tested on: NVIDIA L4 24GB (GCE), NVIDIA A40 48GB (RunPod), Apple M-series (CPU).

Files

Citation

If you use EntropyHunter in your work, please cite:

@misc{duzkar2026entropyhunter,
  title={EntropyHunter: A Fine-Tuned LLM for Industrial Exergy Analysis},
  author={Düzkar, Kemal},
  year={2026},
  url={https://huggingface.co/olivenet/entropy-hunter-8b-gguf}
}

About

Built by Kemal Düzkar at Olivenet (KKTC). EntropyHunter is part of a larger vision: combining IoT sensing with deep second-law thermodynamic analysis to help industrial facilities find and eliminate exergy destruction — the hidden inefficiencies that first-law analysis misses.

ExergyLab (36,000+ lines, 7 analysis engines) provides the domain foundation. EntropyHunter packages that expertise into an edge-deployable AI model.

Related

• —Training Data Preview: olivenet/entropy-hunter-dataset-preview (10 examples)

License

Apache 2.0