Instructions to use PramodSeeram/BFSI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use PramodSeeram/BFSI 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 PramodSeeram/BFSI # Run inference directly in the terminal: llama cli -hf PramodSeeram/BFSI
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf PramodSeeram/BFSI # Run inference directly in the terminal: llama cli -hf PramodSeeram/BFSI
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 PramodSeeram/BFSI # Run inference directly in the terminal: ./llama-cli -hf PramodSeeram/BFSI
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 PramodSeeram/BFSI # Run inference directly in the terminal: ./build/bin/llama-cli -hf PramodSeeram/BFSI
Use Docker
docker model run hf.co/PramodSeeram/BFSI
- LM Studio
- Jan
- Ollama
How to use PramodSeeram/BFSI with Ollama:
ollama run hf.co/PramodSeeram/BFSI
- Unsloth Desktop
- Docker Model Runner
How to use PramodSeeram/BFSI with Docker Model Runner:
docker model run hf.co/PramodSeeram/BFSI
- Lemonade
How to use PramodSeeram/BFSI with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull PramodSeeram/BFSI
Run and chat with the model
lemonade run user.BFSI-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Upload README.md with huggingface_hub
Browse files
README.md
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# Astra LLM (BFSI Domain)
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## Overview
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A domain-adapted Large Language Model built on Mistral 7B, fine-tuned using LoRA for financial intelligence tasks.
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## Capabilities
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- Credit card optimization
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- Financial insights & recommendations
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- Fraud/scam reasoning
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- Insurance claim understanding
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## Architecture
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- Base Model: Mistral 7B
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- Fine-tuning: LoRA
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- Deployment: Ollama
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- Format: GGUF
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## Integration
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- RAG with Qdrant
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- Multi-agent orchestration (LangGraph)
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- Used in Astra 360 Digital Brain
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## Usage (Ollama)
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ollama run astra-llm
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## Author
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Pramod Seeram
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