Instructions to use MrinalKumar/finance-analyzer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MrinalKumar/finance-analyzer with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("MrinalKumar/finance-analyzer", dtype="auto")

llama-cpp-python

How to use MrinalKumar/finance-analyzer with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="MrinalKumar/finance-analyzer",
	filename="unsloth.Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use MrinalKumar/finance-analyzer with 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:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MrinalKumar/finance-analyzer: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:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MrinalKumar/finance-analyzer: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:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MrinalKumar/finance-analyzer: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:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MrinalKumar/finance-analyzer:Q4_K_M

Use Docker

docker model run hf.co/MrinalKumar/finance-analyzer:Q4_K_M

LM Studio
Jan
Ollama
How to use MrinalKumar/finance-analyzer with Ollama:
```
ollama run hf.co/MrinalKumar/finance-analyzer:Q4_K_M
```

Unsloth Studio

How to use MrinalKumar/finance-analyzer with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MrinalKumar/finance-analyzer to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MrinalKumar/finance-analyzer to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MrinalKumar/finance-analyzer to start chatting

Atomic Chat new
Docker Model Runner
How to use MrinalKumar/finance-analyzer with Docker Model Runner:
```
docker model run hf.co/MrinalKumar/finance-analyzer:Q4_K_M
```

Lemonade

How to use MrinalKumar/finance-analyzer with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MrinalKumar/finance-analyzer:Q4_K_M

Run and chat with the model

lemonade run user.finance-analyzer-Q4_K_M

List all available models

lemonade list

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

Downloads last month: 20

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

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