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"

olivenet commited on Mar 2

Commit

618c06c

verified ·

1 Parent(s): e90b707

Update README.md

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README.md +3 -5

README.md CHANGED Viewed

@@ -23,7 +23,7 @@ datasets:
 # EntropyHunter-8B v0.4 — Exergy Analysis Specialist
-**The first 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.
@@ -45,7 +45,6 @@ EntropyHunter performs rigorous exergy calculations including energy/exergy bala
 | Quantization | Q4_K_M (4.7 GB) |
 | Context Length | 8,192 tokens |
 | Training Loss | 0.408 |
-| Training Cost | ~$2 GPU + ~$210 data generation |
 | Thinking Mode | Disabled (direct response, no chain-of-thought tokens) |
 ## Supported Analysis Types
@@ -106,7 +105,7 @@ SYSTEM """You are an expert thermodynamics engineer specializing in exergy (seco
 Claude Opus 4.6 (Teacher)
     │
     ▼
-1,500 examples generated (Batch API, ~$210)
     │
     ▼
 Thermodynamic QC Pipeline
@@ -124,7 +123,6 @@ Thermodynamic QC Pipeline
 LoRA Fine-tuning (Qwen3-8B, Unsloth)
 ├── r=16, α=32, lr=1e-4
 ├── 3 epochs, 465 steps
-├── A40 48GB GPU, 5 hours
 └── Training loss: 0.408
     │
     ▼
@@ -164,7 +162,7 @@ GGUF Export (Q4_K_M, 4.7 GB)
 ## About
-Built by [Kemal Düzkar](https://www.linkedin.com/in/kemal-d%C3%BCzkar/), chemical engineer and founder of [Olivenet](https://olivenet.io) (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.*

 # 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.
 | 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
 Claude Opus 4.6 (Teacher)
     │
     ▼
+1,500 examples generated (Batch API)
     │
     ▼
 Thermodynamic QC Pipeline
 LoRA Fine-tuning (Qwen3-8B, Unsloth)
 ├── r=16, α=32, lr=1e-4
 ├── 3 epochs, 465 steps
 └── Training loss: 0.408
     │
     ▼
 ## About
+Built by [Kemal Düzkar](https://www.linkedin.com/in/kemalduzkar/), chemical engineer and founder of [Olivenet](https://olivenet.io) (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.*