Update README.md

README.md

@@ -1,60 +1,58 @@
 ---
-library_name: transformers
 tags:
 - sft
 - unsloth
 - finance
-- llama-cpp
-- gguf-my-repo
 license: apache-2.0
 datasets:
 - atahanuz/stock_prediction
 language:
 - en
-base_model: khazarai/StockDirection-6K
+base_model:
+- unsloth/Qwen3-0.6B
 pipeline_tag: text-generation
 ---
 
-# Rustamshry/StockDirection-6K-Q8_0-GGUF
-This model was converted to GGUF format from [`khazarai/StockDirection-6K`](https://huggingface.co/khazarai/StockDirection-6K) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
-Refer to the [original model card](https://huggingface.co/khazarai/StockDirection-6K) for more details on the model.
-
-## Use with llama.cpp
-Install llama.cpp through brew (works on Mac and Linux)
-
-```bash
-brew install llama.cpp
-
-```
-Invoke the llama.cpp server or the CLI.
-
-### CLI:
-```bash
-llama-cli --hf-repo Rustamshry/StockDirection-6K-Q8_0-GGUF --hf-file stockdirection-6k-q8_0.gguf -p "The meaning to life and the universe is"
-```
-
-### Server:
-```bash
-llama-server --hf-repo Rustamshry/StockDirection-6K-Q8_0-GGUF --hf-file stockdirection-6k-q8_0.gguf -c 2048
-```
-
-Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
-
-Step 1: Clone llama.cpp from GitHub.
-```
-git clone https://github.com/ggerganov/llama.cpp
-```
-
-Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
-```
-cd llama.cpp && LLAMA_CURL=1 make
-```
-
-Step 3: Run inference through the main binary.
-```
-./llama-cli --hf-repo Rustamshry/StockDirection-6K-Q8_0-GGUF --hf-file stockdirection-6k-q8_0.gguf -p "The meaning to life and the universe is"
-```
-or
-```
-./llama-server --hf-repo Rustamshry/StockDirection-6K-Q8_0-GGUF --hf-file stockdirection-6k-q8_0.gguf -c 2048
-```
+# 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.
+
+## Training Data
+
+- Dataset: [atahanuz/stock_prediction](https://huggingface.co/datasets/atahanuz/stock_prediction)
+- Size: 6,355 labeled examples.
+- Structure: Each sample contains past 5 daily percentage changes and the target label (UP/DOWN).
+
+Example:
+
+```vbnet
+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
+```