Text Generation
GGUF
Safetensors
English
Hindi
gemma
gemma-3
gemma-4
finetune
distillation
on-device
android
llama-cpp
transaction-parsing
json-output
structured-output
voice-input
expense-tracking
code-mixed
hinglish
Eval Results (legacy)
conversational
Instructions to use kartikey31/txn-parser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use kartikey31/txn-parser with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="kartikey31/txn-parser", filename="student/gguf/gemma-3-270m-it.BF16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use kartikey31/txn-parser with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf kartikey31/txn-parser:Q4_K_M # Run inference directly in the terminal: llama-cli -hf kartikey31/txn-parser:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf kartikey31/txn-parser:Q4_K_M # Run inference directly in the terminal: llama-cli -hf kartikey31/txn-parser: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 kartikey31/txn-parser:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf kartikey31/txn-parser: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 kartikey31/txn-parser:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf kartikey31/txn-parser:Q4_K_M
Use Docker
docker model run hf.co/kartikey31/txn-parser:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use kartikey31/txn-parser with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kartikey31/txn-parser" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kartikey31/txn-parser", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/kartikey31/txn-parser:Q4_K_M
- Ollama
How to use kartikey31/txn-parser with Ollama:
ollama run hf.co/kartikey31/txn-parser:Q4_K_M
- Unsloth Studio new
How to use kartikey31/txn-parser 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 kartikey31/txn-parser 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 kartikey31/txn-parser to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for kartikey31/txn-parser to start chatting
- Docker Model Runner
How to use kartikey31/txn-parser with Docker Model Runner:
docker model run hf.co/kartikey31/txn-parser:Q4_K_M
- Lemonade
How to use kartikey31/txn-parser with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull kartikey31/txn-parser:Q4_K_M
Run and chat with the model
lemonade run user.txn-parser-Q4_K_M
List all available models
lemonade list
Ctrl+K