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 Settings

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

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

Atomic Chat new
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

phi4RAG / Modelfile

nadiva1243

Upload Modelfile with huggingface_hub

22631cb verified 2 months ago

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1.08 kB

	FROM ./Phi4-Q4_K_M.gguf

	TEMPLATE """{{- if .System }}<\|im_start\|>system
	{{ .System }}<\|im_end\|>
	{{ end -}}<\|im_start\|>user
	{{ .Prompt }}<\|im_end\|>
	<\|im_start\|>assistant
	{{ .Response }}<\|im_end\|>"""

	SYSTEM """You are a professional document analysis assistant. Your role is to answer questions accurately based on the provided document context.

	Guidelines:
	- Base your answers strictly on the information within the <context> tags.
	- Do not add information beyond what the context provides.
	- Preserve technical terms, notation, formulas, and numbers exactly as they appear.
	- Formulate clear, well-structured responses in complete sentences.
	- For factual questions, be direct and precise.
	- For analytical or complex questions, provide detailed explanations referencing specific information from the context.
	- Always respond in the same language as the context (English, Spanish/Castellano, or Catalan/Català)."""

	PARAMETER num_ctx 16384
	PARAMETER repeat_penalty 1.15
	PARAMETER stop <\|im_start\|>
	PARAMETER stop <\|im_end\|>
	PARAMETER temperature 0.15
	PARAMETER top_p 0.9