Instructions to use Rustamshry/StockDirection-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rustamshry/StockDirection-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Rustamshry/StockDirection-GGUF",
	filename="stockdirection-6k-q8_0.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Rustamshry/StockDirection-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Rustamshry/StockDirection-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Rustamshry/StockDirection-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Rustamshry/StockDirection-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Rustamshry/StockDirection-GGUF:Q8_0

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 Rustamshry/StockDirection-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Rustamshry/StockDirection-GGUF:Q8_0

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 Rustamshry/StockDirection-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Rustamshry/StockDirection-GGUF:Q8_0

Use Docker

docker model run hf.co/Rustamshry/StockDirection-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use Rustamshry/StockDirection-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rustamshry/StockDirection-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": "Rustamshry/StockDirection-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Rustamshry/StockDirection-GGUF:Q8_0

Ollama
How to use Rustamshry/StockDirection-GGUF with Ollama:
```
ollama run hf.co/Rustamshry/StockDirection-GGUF:Q8_0
```

Unsloth Studio new

How to use Rustamshry/StockDirection-GGUF 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 Rustamshry/StockDirection-GGUF 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 Rustamshry/StockDirection-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Rustamshry/StockDirection-GGUF to start chatting

Pi new

How to use Rustamshry/StockDirection-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Rustamshry/StockDirection-GGUF:Q8_0

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": "Rustamshry/StockDirection-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Rustamshry/StockDirection-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Rustamshry/StockDirection-GGUF:Q8_0

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 Rustamshry/StockDirection-GGUF:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use Rustamshry/StockDirection-GGUF with Docker Model Runner:
```
docker model run hf.co/Rustamshry/StockDirection-GGUF:Q8_0
```

Lemonade

How to use Rustamshry/StockDirection-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Rustamshry/StockDirection-GGUF:Q8_0

Run and chat with the model

lemonade run user.StockDirection-GGUF-Q8_0

List all available models

lemonade list

Model Card for StockDirection-6K

Model Details

GGUF version of https://huggingface.co/khazarai/StockDirection

StockDirection is a fine-tuned language model for binary stock movement prediction. The model is trained to predict whether the next day’s stock price of Akbank (AKBNK), traded on Borsa Istanbul (BIST), will move UP or DOWN, based on the daily percentage changes from the last four days and the current day.

Input: A formatted prompt describing the last 5 days of daily percentage price changes.
Output: A simple categorical prediction — "UP" or "DOWN".

This model was fine-tuned on a dataset of 6,300 labeled rows of AKBNK stock data.

Uses

Direct Use

Educational purposes: Demonstrating how LLMs can be fine-tuned for financial classification tasks.
Research: Exploring text-based sequence learning for stock direction prediction.
Proof of concept: Serving as an example for stock price direction prediction using natural language prompts.

⚠️ Not for financial advice or live trading decisions.

Training Data

Dataset: atahanuz/stock_prediction
Size: 6,355 labeled examples.
Structure: Each sample contains past 5 daily percentage changes and the target label (UP/DOWN).

Example:

Question: You are an assistant that predicts whether a stock will go up or down in the next day 
based on the daily percentage price changes of the last:
4 days ago: nan
3 days ago: 0.00
2 days ago: 2.22
1 day ago: -2.17
today: -2.22
Predict whether the next day's price will go up or down. 
Simply write your prediction as UP or DOWN.

Answer: DOWN