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MrinalKumar
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finance-analyzer-V2

Transformers
GGUF
English
qwen2
text-generation-inference
unsloth
Model card Files
xet
 Community
1
How to use from
llama.cpp
Install (macOS, Linux) 
curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MrinalKumar/finance-analyzer-V2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MrinalKumar/finance-analyzer-V2:Q4_K_M
Install from WinGet (Windows) 
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MrinalKumar/finance-analyzer-V2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MrinalKumar/finance-analyzer-V2:Q4_K_M
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 MrinalKumar/finance-analyzer-V2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MrinalKumar/finance-analyzer-V2:Q4_K_M
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 MrinalKumar/finance-analyzer-V2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MrinalKumar/finance-analyzer-V2:Q4_K_M
Use Docker 
docker model run hf.co/MrinalKumar/finance-analyzer-V2:Q4_K_M
Quick Links

Finance Earnings Call Q&A Bot

Fine-tuned LLM for financial question-answering and earnings call simulation
By Mrinal Kumar

🚀 Model Overview

This model is a quantized [Qwen-2.5B / your base model] transformer fine-tuned on real earnings call transcripts and Q&A pairs from S&P 500 companies and global markets. It is designed to:

  • Summarize complex financial calls into key insights
  • Simulate Q&A between analysts and CFOs/CEOs

🏆 Example Use Cases

  • Students: Learn how real-world analysts and CFOs communicate
  • Investors: Get concise summaries or simulate earnings call Q&A
  • Researchers: Build finance chatbots or extract structured knowledge from transcripts

🗂️ Training Data

  • Dataset: Manually curated Q&A pairs extracted from publicly available earnings calls (Kaggle Earnings Call Datasets)
  • Format: Each example consists of an analyst question (input) and a CFO/CEO response (output)
  • Size: 1,000+ Q&A pairs for diverse scenarios

💡 Example Questions

Analyst Question Model Response
What drove the 20% YoY revenue growth? Revenue growth was driven by new subscriptions and higher pricing.
Can you explain margin contraction in the EU market? Margins contracted due to logistics costs and currency headwinds.
What are your key risks for next quarter? Potential supply-chain delays and FX volatility.

🔬 Training Details

  • Base model: Qwen-2.5B quantized GGUF
  • Environment: Google Colab, 4-bit quantization for memory efficiency
  • Optimization: Fine-tuned using Unsloth/PEFT on curated JSONL dataset

🤝 Acknowledgements

  • Hugging Face & Kaggle for model hosting and data
  • Open source communities for technical guidance

🌐 License

Apache 2.0

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GGUF
Model size
8B params
Architecture
qwen2
Hardware compatibility
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4-bit

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