qwen3-14b-turkish-finance

Qwen3-14B Turkish Finance

A fine-tuned version of Qwen3-14B specialized in Turkish and global financial markets, including cryptocurrency, stock markets, technical analysis, fundamental analysis, and risk management — with a focus on the Turkish investor perspective.

Model Details

Intended Use

This model is designed for:

• Answering questions about cryptocurrency ecosystems (Bitcoin, Ethereum, DeFi, Layer 2, stablecoins)
• Providing insights on Turkish stock markets (BIST 100, BIST 30, sector indices)
• Discussing global markets (NASDAQ, S&P 500, DAX, Nikkei)
• Explaining technical analysis concepts (chart patterns, indicators, Fibonacci, Elliott Wave)
• Explaining fundamental analysis (DCF, P/E, EV/EBITDA, tokenomics)
• Guiding on risk management (portfolio theory, position sizing, hedging)
• Explaining regulations relevant to Turkish and global investors (SPK, SEC, MiCA)

⚠️ Disclaimer: This model is intended for educational and informational purposes only. It does not provide investment advice. Always consult a licensed financial advisor before making investment decisions.

How to Use

Load the Model

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name     = "efedemircan/qwen3-14b-turkish-finance",
    max_seq_length = 8192,
    load_in_4bit   = True,
)
FastLanguageModel.for_inference(model)

Inference (Recommended — Non-Thinking Mode)

messages = [
    {
        "role": "user",
        "content": "Türkiye'de enflasyonist ortamlarda yatırımcılar hangi varlık sınıflarına yönelir?"
    }
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize              = False,
    add_generation_prompt = True,
    enable_thinking       = False,  # Fast, direct responses
)

inputs = tokenizer(text, return_tensors="pt").to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens     = 2048,
    temperature        = 0.7,
    top_p              = 0.8,
    top_k              = 20,
    repetition_penalty = 1.1,
)

response = tokenizer.decode(
    outputs[0][inputs["input_ids"].shape[1]:],
    skip_special_tokens=True
)
print(response)

Inference (Thinking Mode)

For more complex analytical questions, enable the thinking mode to allow chain-of-thought reasoning:

text = tokenizer.apply_chat_template(
    messages,
    tokenize              = False,
    add_generation_prompt = True,
    enable_thinking       = True,   # Enables <think> block
)

outputs = model.generate(
    **inputs,
    max_new_tokens     = 8192,
    temperature        = 0.6,
    top_p              = 0.95,
    top_k              = 20,
    repetition_penalty = 1.15,
)

Training Details

Dataset

The model was fine-tuned on the Turkish Finance SFT Dataset, which contains 2,397 high-quality Turkish finance Q&A pairs covering:

• Cryptocurrency ecosystems and on-chain analysis
• BIST and global stock markets
• Technical and fundamental analysis
• Risk management strategies
• Turkish and global financial regulations

Limitations

• The model may occasionally produce hallucinated financial data (e.g., incorrect ticker codes, non-existent products). Always verify specific figures and product names from official sources.
• The training dataset contains 2,397 examples, which is relatively small. Performance on highly niche or rarely discussed topics may be limited.
• The model reflects knowledge present in the training dataset and the base Qwen3-14B model. It may not reflect real-time market data or recent regulatory changes.
• Not suitable for automated trading systems or any application where financial accuracy is mission-critical.

Citation

If you use this model, please cite both the base model and the training dataset:

@dataset{kiraz2025turkishfinance,
  title     = {Turkish Finance SFT Dataset},
  author    = {Kiraz, Alican},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/datasets/AlicanKiraz0/Turkish-Finance-SFT-Dataset}
}

@misc{qwen3-14b-turkish-finance,
  title  = {Qwen3-14B Turkish Finance},
  author = {Demircan, Efe},
  year   = {2026},
  url    = {https://huggingface.co/efedemircan/qwen3-14b-turkish-finance}
}

Acknowledgements

• Unsloth for the efficient fine-tuning framework
• Qwen Team for the base Qwen3-14B model
• Alican Kiraz for the Turkish Finance SFT Dataset

Uploaded model

• Developed by: efedemircan
• License: apache-2.0
• Finetuned from model : unsloth/qwen3-14b-unsloth-bnb-4bit

This qwen3 model was trained 2x faster with Unsloth

<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>