Instructions to use olivenet/entropy-hunter-8b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use olivenet/entropy-hunter-8b-gguf with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="olivenet/entropy-hunter-8b-gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("olivenet/entropy-hunter-8b-gguf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use olivenet/entropy-hunter-8b-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf olivenet/entropy-hunter-8b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf olivenet/entropy-hunter-8b-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf olivenet/entropy-hunter-8b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf olivenet/entropy-hunter-8b-gguf:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf olivenet/entropy-hunter-8b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf olivenet/entropy-hunter-8b-gguf:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf olivenet/entropy-hunter-8b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf olivenet/entropy-hunter-8b-gguf:Q4_K_M

Use Docker

docker model run hf.co/olivenet/entropy-hunter-8b-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use olivenet/entropy-hunter-8b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "olivenet/entropy-hunter-8b-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "olivenet/entropy-hunter-8b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/olivenet/entropy-hunter-8b-gguf:Q4_K_M

SGLang

How to use olivenet/entropy-hunter-8b-gguf with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "olivenet/entropy-hunter-8b-gguf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "olivenet/entropy-hunter-8b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "olivenet/entropy-hunter-8b-gguf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "olivenet/entropy-hunter-8b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use olivenet/entropy-hunter-8b-gguf with Ollama:
```
ollama run hf.co/olivenet/entropy-hunter-8b-gguf:Q4_K_M
```
Unsloth Desktop

How to use olivenet/entropy-hunter-8b-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf olivenet/entropy-hunter-8b-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "olivenet/entropy-hunter-8b-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use olivenet/entropy-hunter-8b-gguf with Docker Model Runner:
```
docker model run hf.co/olivenet/entropy-hunter-8b-gguf:Q4_K_M
```

Lemonade

How to use olivenet/entropy-hunter-8b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull olivenet/entropy-hunter-8b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.entropy-hunter-8b-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use olivenet/entropy-hunter-8b-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf olivenet/entropy-hunter-8b-gguf:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default olivenet/entropy-hunter-8b-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use olivenet/entropy-hunter-8b-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf olivenet/entropy-hunter-8b-gguf:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "olivenet/entropy-hunter-8b-gguf:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

entropy-hunter-8b-gguf / README.md

olivenet

Update README.md

618c06c verified 7 months ago

6.25 kB

language:
  - en
license: apache-2.0
tags:
  - thermodynamics
  - exergy-analysis
  - second-law
  - industrial-energy
  - domain-specific
  - fine-tuned
  - gguf
  - ollama
  - edge-deployment
base_model: Qwen/Qwen3-8B
model_type: causal-lm
quantized_by: unsloth
pipeline_tag: text-generation
library_name: llama.cpp
datasets:
  - custom

EntropyHunter-8B v0.4 — Exergy Analysis Specialist

A fine-tuned LLM specialized in second-law thermodynamic (exergy) analysis of industrial equipment.

EntropyHunter performs rigorous exergy calculations including energy/exergy balances, entropy generation, exergoeconomic analysis, and thermodynamic optimization — tasks that general-purpose LLMs struggle with despite their broad knowledge.

Key Features

Domain Expert: Trained specifically for exergy (second-law) analysis across 7 equipment types and 6 analysis categories
Verified Training Data: 1,369 examples generated by Claude Opus 4.6 and validated through thermodynamic consistency checks (energy balance, Gouy-Stodola, Bejan number)
Edge Deployable: 8B parameters, Q4_K_M quantization (4.7 GB) — runs on laptops, Jetson Orin, and industrial edge devices without cloud connectivity
No Cloud Required: Industrial data stays on-premise — EU data sovereignty compliant

Model Details

Property	Value
Base Model	Qwen3-8B
Fine-tuning	LoRA (r=16, α=32) via Unsloth
Training Data	1,235 train / 134 val examples (ChatML)
Teacher Model	Claude Opus 4.6 (knowledge distillation)
Quantization	Q4_K_M (4.7 GB)
Context Length	8,192 tokens
Training Loss	0.408
Thinking Mode	Disabled (direct response, no chain-of-thought tokens)

Supported Analysis Types

Analysis	Description
Basic Exergy	Exergy destruction, exergetic efficiency, irreversibility
Entropy Generation	Entropy generation mechanisms, Gouy-Stodola theorem
Exergoeconomic	Cost of exergy destruction, thermoeconomic evaluation
What-If Comparison	Parameter sensitivity, operating condition optimization
Avoidable/Unavoidable	Splitting exergy destruction into improvement potential
Hotspot Detection	Multi-equipment system bottleneck identification

Supported Equipment

Compressors, heat exchangers, boilers, steam turbines, pumps, chillers, dryers, and multi-equipment factory systems — across 48 industrial subtypes.

Quick Start — Ollama

# Download and create model
ollama create entropy-hunter -f Modelfile

# Run
ollama run entropy-hunter "Perform exergy analysis for a centrifugal compressor with inlet air at 25°C, 101.325 kPa; outlet at 215°C, 800 kPa; mass flow 2.5 kg/s; power input 520 kW; isentropic efficiency 78%. Dead state: T₀ = 25°C, P₀ = 101.325 kPa."

Modelfile

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

PARAMETER temperature 0.7
PARAMETER top_p 0.8
PARAMETER top_k 20
PARAMETER num_ctx 8192
PARAMETER stop <|im_end|>
PARAMETER stop <|endoftext|>

SYSTEM """You are an expert thermodynamics engineer specializing in exergy (second-law) analysis of industrial equipment. You perform rigorous calculations using dead state conditions T₀ = 25°C (298.15 K), P₀ = 101.325 kPa. /no_think"""

Quick Start — llama.cpp

./llama-cli \
  --model entropy-hunter-v04-Q4_K_M.gguf \
  --ctx-size 8192 \
  --temp 0.7 \
  --top-p 0.8 \
  --top-k 20 \
  -p "Perform exergy analysis for a shell-and-tube heat exchanger..."

Training Pipeline

Claude Opus 4.6 (Teacher)
    │
    ▼
1,500 examples generated (Batch API)
    │
    ▼
Thermodynamic QC Pipeline
├── Energy balance verification (±1%)
├── Gouy-Stodola consistency check
├── Bejan number range (0-1)
├── Exergoeconomic f-factor validation
├── Self-correction aware parsing
└── Balance-line fallback extraction
    │
    ▼
1,369 verified examples (93.4% pass rate)
    │
    ▼
LoRA Fine-tuning (Qwen3-8B, Unsloth)
├── r=16, α=32, lr=1e-4
├── 3 epochs, 465 steps
└── Training loss: 0.408
    │
    ▼
GGUF Export (Q4_K_M, 4.7 GB)

Version History

Version	Base Model	Data	Score	Notes
v0.1	Qwen2.5-7B	722 examples	63.5%	First attempt, structural learning
v0.2	Qwen2.5-7B	885 examples	85.5%	Stable, "mention vs calculate" fix
v0.3	Qwen2.5-7B	885 (JSON-free)	78.3%	Failed experiment, archived
v0.4	Qwen3-8B	1,369 examples	TBD	Current — scaffold format, verified distillation

Base Model Comparison (Pre-v0.4)

Model	Benchmark Score	Notes
Base Qwen2.5-7B	68.4%	No fine-tuning
Base Qwen3-8B	82.6%	No fine-tuning
Fine-tuned v0.2 (Qwen2.5-7B)	85.5%	885 examples

Limitations

Arithmetic: 8B models have inherent arithmetic inconsistency — same inputs can produce slightly different numerical results across runs
JSON Output: Cannot reliably produce structured JSON (known 7B/8B limitation)
Language: English only (optimized for tokenizer efficiency)
Scope: Industrial equipment exergy analysis only — not a general thermodynamics tutor

Intended Use

Industrial energy auditors performing second-law analysis
Engineers identifying thermodynamic inefficiencies in equipment
Edge deployment for real-time exergy monitoring (IoT integration)
Educational tool for exergy analysis methodology

About

Built by Kemal Düzkar, chemical engineer and founder of Olivenet (Northern Cyprus). EntropyHunter is part of the ExergyLab ecosystem — bringing thermodynamic intelligence to industrial IoT.

Philosophy: Teaching a small model what only large models know — so it can run where large models can't.

Citation

@misc{entropyhunter2026,
  title={EntropyHunter-8B: A Domain-Specific LLM for Industrial Exergy Analysis},
  author={Düzkar, Kemal},
  year={2026},
  url={https://huggingface.co/olivenet/entropy-hunter-8b-gguf}
}

License

Apache 2.0