Instructions to use saidutta69/Ghosty-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saidutta69/Ghosty-7B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="saidutta69/Ghosty-7B",
	filename="ghosty-7b-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use saidutta69/Ghosty-7B 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 saidutta69/Ghosty-7B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saidutta69/Ghosty-7B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf saidutta69/Ghosty-7B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saidutta69/Ghosty-7B: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 saidutta69/Ghosty-7B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf saidutta69/Ghosty-7B: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 saidutta69/Ghosty-7B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf saidutta69/Ghosty-7B:Q4_K_M

Use Docker

docker model run hf.co/saidutta69/Ghosty-7B:Q4_K_M

LM Studio
Jan

vLLM

How to use saidutta69/Ghosty-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "saidutta69/Ghosty-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saidutta69/Ghosty-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/saidutta69/Ghosty-7B:Q4_K_M

Ollama
How to use saidutta69/Ghosty-7B with Ollama:
```
ollama run hf.co/saidutta69/Ghosty-7B:Q4_K_M
```

Unsloth Studio

How to use saidutta69/Ghosty-7B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for saidutta69/Ghosty-7B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for saidutta69/Ghosty-7B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for saidutta69/Ghosty-7B to start chatting

How to use saidutta69/Ghosty-7B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf saidutta69/Ghosty-7B:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "saidutta69/Ghosty-7B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use saidutta69/Ghosty-7B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf saidutta69/Ghosty-7B: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 saidutta69/Ghosty-7B:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use saidutta69/Ghosty-7B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf saidutta69/Ghosty-7B: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 "saidutta69/Ghosty-7B: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"

Docker Model Runner
How to use saidutta69/Ghosty-7B with Docker Model Runner:
```
docker model run hf.co/saidutta69/Ghosty-7B:Q4_K_M
```

Lemonade

How to use saidutta69/Ghosty-7B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull saidutta69/Ghosty-7B:Q4_K_M

Run and chat with the model

lemonade run user.Ghosty-7B-Q4_K_M

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Ghosty-7B 👻

A Paranormal Investigator LLM — fine-tuned from Qwen2.5-7B-Instruct on a curated corpus of Indian paranormal encounter transcripts, investigator blogs, and first-person supernatural experience accounts.

Designed to respond like an experienced paranormal investigator: open-minded yet analytical, fluent in Hinglish, and respectful of both scientific inquiry and lived supernatural experiences.

Model Capabilities

🎙️ Hinglish-first: Responds naturally in Hindi-English code-mixing, matching how Indian paranormal communities communicate
🔍 Investigator persona: Balances skepticism with belief — investigates claims seriously without dismissing or fear-mongering
🏠 Indian context: Understands local ghost lore (bhoot-pret, tantra-mantra, aatma), regional beliefs, and culturally relevant advice
📖 Storytelling: Can narrate Indian folklore and paranormal encounter stories in an engaging first-person investigator style

Example Interaction

User: Maine ek raat ko ghar me ajeeb awaaz suni. Kya ho sakta hai?
Ghosty: Aise ajeeb awaaze ki koi common reasons hote hain:
1. Electrical interference (like faulty appliances)
2. Humming from AC/ventilator
3. Koi animal (katra, bat)
4. Wind noise through windows
...

User: Kya bhoot sach me hote hain?
Ghosty: Bhoot ka concept India ke folklore aur spiritual traditions mein bahut purana hai,
lekin modern scientific perspective se aise entities ko "unexplained phenomena" ya
"paranormal activity" ke roop mein label kiya jata hai...

Training Data

Trained on a diverse collection of:

Paranormal investigator blogs & transcripts (Creative Commons licensed, used with permission)
Indian ghost encounter narratives from verified public sources
First-person supernatural experience accounts from Indian online communities
Hinglish conversational data spanning urban and rural paranormal reporting styles

Dataset: ~3,000 SFT question-answer pairs in Qwen chat format.

All data sourced from publicly available, permissively licensed Indian paranormal content.

Training Details

Parameter	Value
Base Model	Qwen2.5-7B-Instruct (abliterated variant)
Fine-tuning	Full parameter FT (not LoRA)
Hardware	NVIDIA H100 80GB
Precision	BF16
Attention	SDPA (PyTorch built-in)
Epochs	5
Batch Size	8 (effective 16 with grad accum)
Learning Rate	2e-5 (cosine, warmup 185 steps)
Max Length	2048 tokens
Gradient Checkpointing	Yes

Intended Use

Ghosty-7B is designed for:

Paranormal investigation RP/chatbots
Indian folklore & ghost story generation
First-responder paranormal advice systems
Educational demonstrations of Indian supernatural beliefs

Limitations

May occasionally hallucinate specific case details (standard for LLMs)
Best at Hinglish — performance in pure English or pure Hindi may vary
Not a replacement for professional mental health or law enforcement advice
Trained on publicly shared experiences — individual accounts may not be verified

License

Apache 2.0

Downloads last month: 664

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for saidutta69/Ghosty-7B

Quantizations

2 models

saidutta69
/

Ghosty-7B

Ghosty-7B 👻

Model Capabilities

Example Interaction

Training Data

Training Details

Intended Use

Limitations

License

Model tree for saidutta69/Ghosty-7B

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