Text Generation
Transformers
Safetensors
English
qwen3
sft
unsloth
finance
conversational
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("khazarai/StockDirection-6K")
model = AutoModelForCausalLM.from_pretrained("khazarai/StockDirection-6K")
messages = [
{"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))Quick Links
Model Card for StockDirection-6K
Model Details
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.
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("khazarai/StockDirection-6K")
model = AutoModelForCausalLM.from_pretrained(
"khazarai/StockDirection-6K",
device_map={"": 0}
)
question ="""
You are an assistant that predicts whatever a stock will go up or down in the next day based on the daily percentage price changes of the last:
4 days ago: 0.00
3 days ago: -3.09
2 days ago: 2.13
1 day ago: -2.04
today: 0.01
Predict whatever the next day's price will go up or down. Simply write your prediction as UP or DOWN
"""
messages = [
{"role" : "user", "content" : question}
]
text = tokenizer.apply_chat_template(
messages,
tokenize = False,
add_generation_prompt = True,
enable_thinking = False,
)
from transformers import TextStreamer
_ = model.generate(
**tokenizer(text, return_tensors = "pt").to("cuda"),
max_new_tokens = 200,
temperature = 0.7,
top_p = 0.8,
top_k = 20,
streamer = TextStreamer(tokenizer, skip_prompt = True),
)
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
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="khazarai/StockDirection-6K") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)