Text Generation
GGUF
English
Spanish
Catalan
rag
retrieval-augmented-generation
lora
phi4
multilingual
ollama
conversational
Instructions to use nadiva1243/phi4RAG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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 (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
- 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
File size: 1,076 Bytes
22631cb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | 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
|