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

Libraries

How to use nadiva1243/phi4RAG with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nadiva1243/phi4RAG",
	filename="Phi4-Q4_K_M.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 nadiva1243/phi4RAG with llama.cpp:

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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 new

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

nadiva1243 commited on 26 days ago

Commit

defcdbc

verified ·

1 Parent(s): 5f905bd

model card: add github repo link, update source code section

Browse files

Files changed (1) hide show

README.md +9 -3

README.md CHANGED Viewed

@@ -23,9 +23,15 @@ Quantized **GGUF** build of **[microsoft/phi-4](https://huggingface.co/microsoft
 ## Source code, thesis, and contact
-The **full MonkeyGrab application repository is not public yet** (defense / publication timeline). This Hugging Face model repo ships **inference assets** (`Phi4-Q4_K_M.gguf`), the **Ollama `Modelfile`**, and a **`reproduction/`** folder with frozen copies of the training script, merge utility, and **`evaluation_comparison.json`** so methodology and metrics remain auditable without requiring access to the full codebase.
-**Contact:** [nadiva1243@gmail.com](mailto:nadiva1243@gmail.com) for questions about training, evaluation, Ollama usage, or when the full repository will be released.
 **GGUF pipeline (high level):** LoRA fine-tuning on the datasets below → merge with `merge_lora.py` (see `reproduction/`) → GGUF export via the llama.cpp toolchain → **Q4_K_M** quantization. The merge script documents expected paths and flags.
@@ -148,6 +154,6 @@ Generation defaults in the bundled `Modelfile`: `num_ctx` 16384, `temperature` 0
   author       = {nadiva1243},
   year         = {2026},
   howpublished = {Hugging Face: \url{https://huggingface.co/nadiva1243/phi4RAG}},
-  note         = {Base: microsoft/phi-4; training: MonkeyGrab train-phi4.py v1}
 }
 ```

 ## Source code, thesis, and contact
+The full **MonkeyGrab** source code is publicly available at:
+> **[https://github.com/iDiagoValeta/localOllamaRAG](https://github.com/iDiagoValeta/localOllamaRAG)**
+The repository includes the complete RAG pipeline, CLI, web interface, training scripts, evaluation workflows, and documentation for the Bachelor's thesis (TFG) at UPV.
+This Hugging Face model repo ships **inference assets** (`Phi4-Q4_K_M.gguf`), the **Ollama `Modelfile`**, and a **`reproduction/`** folder with frozen copies of the training script, merge utility, and **`evaluation_comparison.json`** so methodology and metrics remain auditable alongside the full codebase.
+**Contact:** [nadiva1243@gmail.com](mailto:nadiva1243@gmail.com) for questions about training, evaluation, or Ollama usage.
 **GGUF pipeline (high level):** LoRA fine-tuning on the datasets below → merge with `merge_lora.py` (see `reproduction/`) → GGUF export via the llama.cpp toolchain → **Q4_K_M** quantization. The merge script documents expected paths and flags.
   author       = {nadiva1243},
   year         = {2026},
   howpublished = {Hugging Face: \url{https://huggingface.co/nadiva1243/phi4RAG}},
+  note         = {Base: microsoft/phi-4; training: MonkeyGrab train-phi4.py v1; source: https://github.com/iDiagoValeta/localOllamaRAG}
 }
 ```