Update README.md

# Qwen Market Prediction Model

This model is fine-tuned from Qwen3-4B-Thinking for NIFTY 50 market prediction, utilizing technical indicators and market data to provide short-term price movement forecasts.

## Model Description

- **Model Type:** Causal Language Model (fine-tuned from Qwen3-4B-Thinking)
- **Language:** English
- **License:** [Your chosen license]
- **Developed by:** [Your name/organization]
- **Finetuning Approach:** LoRA (Low-Rank Adaptation)

### Training Data

This model was trained on historical NIFTY 50 data including:
- Daily price movements
- Technical indicators (RSI, MACD, Bollinger Bands)
- Options data (if applicable)
- Market sentiment metrics (if applicable)

The training set consists of sequences with lookback periods of [X] days to predict next-day market movements.

### Intended Use

This model is designed for:
- Analyzing NIFTY 50 market technical indicators
- Predicting short-term price movements
- Providing reasoning for market predictions
- Generating confidence levels for forecasts

### Limitations

- Predictions are probabilistic and should not be the sole basis for investment decisions
- The model is trained on historical data and may not account for unexpected market events
- Performance may vary during extreme market conditions
- This is an experimental model and should be used with appropriate risk management

## Usage

Here's how to use the model:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("your-username/qwen-market-model", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("your-username/qwen-market-model")

# Format prompt
system_message = "You are an expert financial analyst specializing in NIFTY 50 market prediction."
user_message = """
Current NIFTY: 24500.0
Recent 5-day closes: [24400, 24450, 24480, 24500, 24500]
Recent 3-day returns (%): [0.2, 0.12, 0.08]

Technical Indicators:
- RSI(14): 65.2
- MACD: 45.2, Signal: 42.1
- Bollinger Band Width: 1.8%

Task: Analyze the data and predict next trading day movement.
Provide: 1) Technical analysis, 2) Direction (Up/Down/Flat), 3) Expected % change, 4) Confidence level
"""

messages = [
{"role": "system", "content": system_message},
{"role": "user", "content": user_message}
]

# Format using chat template
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# Generate prediction
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.7)
response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)
Training Details

# Base Model: Qwen3-4B-Thinking
Training Method: LoRA fine-tuning
Hyperparameters:

Learning rate: 2e-4
Epochs: 2.08
LoRA rank: 16
LoRA alpha: 32
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Optimizer: AdamW with cosine learning rate schedule
Precision: BF16/FP16 mixed precision

## Citation
If you use this model in your research or applications, please cite:


@misc
{qwen-market-model,
author = {Afzalur Rahman},
title = {Qwen Market Prediction Model},
year = {2025},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/afzalur/qwen-market-model}}
}
## Contact
email: [email protected]; [email protected]

README.md

@@ -1,3 +1,20 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+metrics:
+- accuracy
+- mae
+- mse
+base_model:
+- Qwen/Qwen3-4B-Thinking-2507
+library_name: transformers
+tags:
+- finan
+- market-prediction
+- stock-market
+- nifty50
+- technical-analysis
+- qwen
+- fine-tuned
+---