Instructions to use NisalDeZoysa/Qwen-4B-FirstAid-LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NisalDeZoysa/Qwen-4B-FirstAid-LLM with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("NisalDeZoysa/Qwen-4B-FirstAid-LLM", device_map="auto")

llama-cpp-python

How to use NisalDeZoysa/Qwen-4B-FirstAid-LLM with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="NisalDeZoysa/Qwen-4B-FirstAid-LLM",
	filename="unsloth.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 NisalDeZoysa/Qwen-4B-FirstAid-LLM 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 NisalDeZoysa/Qwen-4B-FirstAid-LLM:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NisalDeZoysa/Qwen-4B-FirstAid-LLM:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NisalDeZoysa/Qwen-4B-FirstAid-LLM:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NisalDeZoysa/Qwen-4B-FirstAid-LLM: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 NisalDeZoysa/Qwen-4B-FirstAid-LLM:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NisalDeZoysa/Qwen-4B-FirstAid-LLM: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 NisalDeZoysa/Qwen-4B-FirstAid-LLM:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NisalDeZoysa/Qwen-4B-FirstAid-LLM:Q4_K_M

Use Docker

docker model run hf.co/NisalDeZoysa/Qwen-4B-FirstAid-LLM:Q4_K_M

LM Studio
Jan
Ollama
How to use NisalDeZoysa/Qwen-4B-FirstAid-LLM with Ollama:
```
ollama run hf.co/NisalDeZoysa/Qwen-4B-FirstAid-LLM:Q4_K_M
```

Unsloth Studio

How to use NisalDeZoysa/Qwen-4B-FirstAid-LLM 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 NisalDeZoysa/Qwen-4B-FirstAid-LLM 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 NisalDeZoysa/Qwen-4B-FirstAid-LLM to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NisalDeZoysa/Qwen-4B-FirstAid-LLM to start chatting

How to use NisalDeZoysa/Qwen-4B-FirstAid-LLM with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NisalDeZoysa/Qwen-4B-FirstAid-LLM: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": "NisalDeZoysa/Qwen-4B-FirstAid-LLM:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use NisalDeZoysa/Qwen-4B-FirstAid-LLM with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NisalDeZoysa/Qwen-4B-FirstAid-LLM: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 NisalDeZoysa/Qwen-4B-FirstAid-LLM:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use NisalDeZoysa/Qwen-4B-FirstAid-LLM with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NisalDeZoysa/Qwen-4B-FirstAid-LLM:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "NisalDeZoysa/Qwen-4B-FirstAid-LLM:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use NisalDeZoysa/Qwen-4B-FirstAid-LLM with Docker Model Runner:
```
docker model run hf.co/NisalDeZoysa/Qwen-4B-FirstAid-LLM:Q4_K_M
```

Lemonade

How to use NisalDeZoysa/Qwen-4B-FirstAid-LLM with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NisalDeZoysa/Qwen-4B-FirstAid-LLM:Q4_K_M

Run and chat with the model

lemonade run user.Qwen-4B-FirstAid-LLM-Q4_K_M

List all available models

lemonade list

Uploaded model

Developed by: Team DataMavericks from Faculty of Engineering, University of Ruhuna
License: apache-2.0
Finetuned from model : unsloth/Qwen3-4B-unsloth-bnb-4bit

🩺 FirstAidLLM – Fine-tuned Qwen 4B for First Aid Instructions

📌 Overview

FirstAidLLM is a domain-specialized large language model fine-tuned on the First Aid Instructions Dataset using Qwen 4B as the base model. The goal is to accurately answer medical first aid-related questions in a concise, safe, and reliable way.

🚑 Whether it’s choking, burns, or CPR instructions, FirstAidLLM provides fast and accurate guidance.

✨ Features

✅ Fine-tuned Qwen3-4B using LoRA + 4-bit quantization ✅ Specialized in first aid instructions ✅ Optimized for low GPU memory usage ✅ Compatible with Hugging Face Transformers & TRL SFTTrainer ✅ Easy to deploy on AWS, SageMaker, or local environment

🗂 Dataset

We used the FirstAidInstructionsDataset from Hugging Face.

Field Description question User's first aid-related question answer Accurate instructions to handle the situation category Type of emergency (e.g., choking, CPR) ⚡ Model Architecture Component Details Base Model Qwen3-4B (UnsLoTH) Fine-tuning LoRA (PEFT) Quantization 4-bit (bnb-4bit) Trainer TRL SFTTrainer Framework PyTorch + Transformers

Downloads last month: 58

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

4-bit

Inference Providers NEW

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Model tree for NisalDeZoysa/Qwen-4B-FirstAid-LLM

Base model

Qwen/Qwen3-4B-Base

Finetuned

Qwen/Qwen3-4B

Quantized

unsloth/Qwen3-4B-unsloth-bnb-4bit

Quantized

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Qwen-4B-FirstAid-LLM

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Model tree for NisalDeZoysa/Qwen-4B-FirstAid-LLM

Dataset used to train NisalDeZoysa/Qwen-4B-FirstAid-LLM