Instructions to use nadiva1243/phi4RAG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use nadiva1243/phi4RAG 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 nadiva1243/phi4RAG:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nadiva1243/phi4RAG:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nadiva1243/phi4RAG:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nadiva1243/phi4RAG: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 nadiva1243/phi4RAG:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nadiva1243/phi4RAG: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 nadiva1243/phi4RAG:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nadiva1243/phi4RAG:Q4_K_M

Use Docker

docker model run hf.co/nadiva1243/phi4RAG:Q4_K_M

LM Studio
Jan

vLLM

How to use nadiva1243/phi4RAG with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nadiva1243/phi4RAG"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nadiva1243/phi4RAG",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nadiva1243/phi4RAG:Q4_K_M

Ollama
How to use nadiva1243/phi4RAG with Ollama:
```
ollama run hf.co/nadiva1243/phi4RAG:Q4_K_M
```

Unsloth Studio

How to use nadiva1243/phi4RAG 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 nadiva1243/phi4RAG 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 nadiva1243/phi4RAG to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nadiva1243/phi4RAG to start chatting

Docker Model Runner
How to use nadiva1243/phi4RAG with Docker Model Runner:
```
docker model run hf.co/nadiva1243/phi4RAG:Q4_K_M
```

Lemonade

How to use nadiva1243/phi4RAG with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nadiva1243/phi4RAG:Q4_K_M

Run and chat with the model

lemonade run user.phi4RAG-Q4_K_M

List all available models

lemonade list

Atomic Chat

nadiva1243 commited on Apr 9

Commit

07a684b

verified ·

1 Parent(s): 0f331c9

Upload README.md with huggingface_hub

Browse files

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README.md +1 -3

README.md CHANGED Viewed

@@ -91,11 +91,9 @@ This adapter build inherits constraints from **Microsoft Phi-4** and from the da
 ```bibtex
 @misc{phi4_rag_gguf_monkeygrab,
   title        = {Phi-4 RAG LoRA Fine-tune (Q4_K_M GGUF)},
-  author       = {Nadiv},
   year         = {2026},
   howpublished = {Hugging Face: \url{https://huggingface.co/nadiva1243/phi4RAG}},
   note         = {Base: microsoft/phi-4; training: MonkeyGrab train-phi4.py v1}
 }
 ```
-Adjust `author` and the Hub URL if your username or repo name differs.

 ```bibtex
 @misc{phi4_rag_gguf_monkeygrab,
   title        = {Phi-4 RAG LoRA Fine-tune (Q4_K_M GGUF)},
+  author       = {nadiva1243},
   year         = {2026},
   howpublished = {Hugging Face: \url{https://huggingface.co/nadiva1243/phi4RAG}},
   note         = {Base: microsoft/phi-4; training: MonkeyGrab train-phi4.py v1}
 }
 ```