Instructions to use PranavKeshav/harmony-structurer-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use PranavKeshav/harmony-structurer-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="PranavKeshav/harmony-structurer-gguf", filename="harmony-structurer-Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use PranavKeshav/harmony-structurer-gguf 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 PranavKeshav/harmony-structurer-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf PranavKeshav/harmony-structurer-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf PranavKeshav/harmony-structurer-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf PranavKeshav/harmony-structurer-gguf: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 PranavKeshav/harmony-structurer-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf PranavKeshav/harmony-structurer-gguf: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 PranavKeshav/harmony-structurer-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf PranavKeshav/harmony-structurer-gguf:Q4_K_M
Use Docker
docker model run hf.co/PranavKeshav/harmony-structurer-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use PranavKeshav/harmony-structurer-gguf with Ollama:
ollama run hf.co/PranavKeshav/harmony-structurer-gguf:Q4_K_M
- Unsloth Studio
How to use PranavKeshav/harmony-structurer-gguf 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 PranavKeshav/harmony-structurer-gguf 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 PranavKeshav/harmony-structurer-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for PranavKeshav/harmony-structurer-gguf to start chatting
- Pi
How to use PranavKeshav/harmony-structurer-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf PranavKeshav/harmony-structurer-gguf:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "PranavKeshav/harmony-structurer-gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use PranavKeshav/harmony-structurer-gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf PranavKeshav/harmony-structurer-gguf:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default PranavKeshav/harmony-structurer-gguf:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use PranavKeshav/harmony-structurer-gguf with Docker Model Runner:
docker model run hf.co/PranavKeshav/harmony-structurer-gguf:Q4_K_M
- Lemonade
How to use PranavKeshav/harmony-structurer-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull PranavKeshav/harmony-structurer-gguf:Q4_K_M
Run and chat with the model
lemonade run user.harmony-structurer-gguf-Q4_K_M
List all available models
lemonade list
llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)Harmony Structurer β GGUF
QLoRA fine-tune of Qwen/Qwen2.5-3B-Instruct on the ADE Corpus v2 for clinical entity extraction.
Merged and exported to GGUF for local inference via LM Studio or llama.cpp.
Files
| File | Size | Use |
|---|---|---|
harmony-structurer-Q4_K_M.gguf |
~2 GB | Load this in LM Studio |
harmony-structurer-f16.gguf |
~6 GB | Full precision reference |
LM Studio setup
- Open LM Studio β Search β paste
PranavKeshav/harmony-structurer-gguf - Download
harmony-structurer-Q4_K_M.gguf - Load model β set context length to 4096
- Start local server on port 1234 (default)
The Harmony backend reads LMSTUDIO_BASE_URL=http://localhost:1234/v1 from .env.
Training source
- Adapter:
PranavKeshav/harmony-structurer-qlora-v1 - Dataset:
ade-benchmark-corpus/ade_corpus_v2(4,271 unique sentences, drug/ADE/dosage relations) - Method: QLoRA (4-bit NF4, rank=16, alpha=32) via Unsloth on T4 GPU
- Downloads last month
- 25
4-bit
16-bit
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="PranavKeshav/harmony-structurer-gguf", filename="", )