---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- thermodynamics
- exergy
- energy-engineering
- industrial
- fine-tuned
- gguf
- ollama
base_model: Qwen/Qwen3-8B
datasets:
- custom
model-index:
- name: EntropyHunter v0.4
  results:
  - task:
      type: text-generation
      name: Thermodynamic Exergy Analysis
    metrics:
    - type: accuracy
      value: 92.7
      name: Adjusted Benchmark Score
    - type: accuracy
      value: 76.7
      name: Raw Benchmark Score
pipeline_tag: text-generation
---

# 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).

| Category | Score | vs Base Qwen3-8B | vs v0.2 (Qwen2.5-7B) |
|----------|-------|-------------------|----------------------|
| Avoidable/Unavoidable | **100.0%** | +20.0pp | +10.0pp |
| Exergoeconomic (SPECO) | **97.5%** | +15.0pp | +19.4pp |
| Hotspot Detection | **97.9%** | +12.0pp | +4.1pp |
| What-if Comparison | **97.3%** | +15.0pp | +0.0pp |
| Basic Exergy | **89.7%** | +7.0pp | +2.9pp |
| Entropy Generation (EGM) | **83.3%** | +1.0pp | +8.7pp |

> **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

| Version | Base Model | Examples | Score | Grade |
|---------|-----------|----------|-------|-------|
| v0.1 | Qwen2.5-7B | 722 | 63.5% | D |
| v0.2 | Qwen2.5-7B | 885 | 85.5% | B+ |
| v0.3 (JSON-free) | Qwen2.5-7B | 885 | 78.3% | C+ |
| Base Qwen3-8B | Qwen3-8B | 0 | 82.6% | B |
| **v0.4** | **Qwen3-8B** | **1,235** | **92.7%** | **A-** |

## 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:** Q4_K_M 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. **Basic Exergy Analysis** — Exergy destruction, efficiency, waste stream identification
2. **Exergoeconomic Analysis (SPECO)** — CRF, cost rates, exergoeconomic factor
3. **Entropy Generation Minimization** — S_gen decomposition, Bejan number, thermodynamic grade
4. **What-if Comparison** — Baseline vs scenario with delta analysis and annual savings
5. **Avoidable/Unavoidable Decomposition** — Tsatsaronis method, improvement potential
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

```bash
# 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

```bash
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. **Energy balance:** |Ex_in − Ex_out − Ex_waste − Ex_d| ≤ 2% of Ex_in
2. **Efficiency range:** 0.1% < η_ex < 99.9%
3. **Second law compliance:** Ex_destroyed ≥ 0
4. **Gouy-Stodola consistency:** |T₀ × S_gen − Ex_d| ≤ 2%
5. **Bejan number validity:** 0 ≤ N_s ≤ 1
6. **f-factor validity:** 0 ≤ f ≤ 1
7. **Dead state reference:** T₀ = 298.15 K appears in analysis
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

| Setup | GPU VRAM | Speed |
|-------|----------|-------|
| GPU inference (recommended) | ≥6 GB | ~80-120 tokens/s |
| CPU inference | 8+ GB RAM | ~5-10 tokens/s |

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

## Files

| File | Size | Description |
|------|------|-------------|
| `entropy-hunter-v04-Q4_K_M.gguf` | 4.7 GB | Main model (Q4_K_M quantization) |
| `training_metadata_v04.json` | ~2 KB | Training configuration and stats |
| `lora-v04/` | ~160 MB | LoRA adapter (for re-quantization) |

## Citation

If you use EntropyHunter in your work, please cite:

```bibtex
@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](https://github.com/olivenet-iot) 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](https://huggingface.co/datasets/olivenet/entropy-hunter-dataset-preview) (10 examples)

## License

Apache 2.0