Instructions to use segestic/MediLITE-QA-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use segestic/MediLITE-QA-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 segestic/MediLITE-QA-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf segestic/MediLITE-QA-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 segestic/MediLITE-QA-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf segestic/MediLITE-QA-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 segestic/MediLITE-QA-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf segestic/MediLITE-QA-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 segestic/MediLITE-QA-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf segestic/MediLITE-QA-GGUF:Q4_K_M

Use Docker

docker model run hf.co/segestic/MediLITE-QA-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use segestic/MediLITE-QA-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "segestic/MediLITE-QA-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "segestic/MediLITE-QA-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/segestic/MediLITE-QA-GGUF:Q4_K_M

Ollama
How to use segestic/MediLITE-QA-GGUF with Ollama:
```
ollama run hf.co/segestic/MediLITE-QA-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use segestic/MediLITE-QA-GGUF with Docker Model Runner:
```
docker model run hf.co/segestic/MediLITE-QA-GGUF:Q4_K_M
```

Lemonade

How to use segestic/MediLITE-QA-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull segestic/MediLITE-QA-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MediLITE-QA-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Phi-3.5 Mini Instruct - Medical LoRA (GGUF Quantized)

This repository contains quantized GGUF versions of a LoRA fine-tuned model based on the microsoft/Phi-3.5-mini-instruct base model.

The fine-tuning was performed using the LoRA adapter: segestic/phi3.5-mini-4k-qlora-medical-seg-vall_med

🔧 Description

The .gguf files in this repository are merged and quantized versions of the above base model + LoRA, suitable for use with llama.cpp or other GGUF-compatible inference engines.

Each file corresponds to a different quantization level.

🧠 Intended Use

These models are intended for offline inference in medical question-answering or document understanding tasks using the phi-3.5 base and a custom LoRA.

⚠️ Disclaimer

This model is fine-tuned on a medical dataset, but it is not a certified clinical tool. Use in production or decision-making requires proper validation.

Downloads last month: 131

GGUF

Model size

4B params

Architecture

phi3

Hardware compatibility

4-bit

5-bit

8-bit

32-bit