Instructions to use hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF", dtype="auto") - llama-cpp-python
How to use hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF", filename="phi-3.5-mini-instruct-iq4_nl-imat.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 hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF:IQ4_NL # Run inference directly in the terminal: llama-cli -hf hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF:IQ4_NL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF:IQ4_NL # Run inference directly in the terminal: llama-cli -hf hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF:IQ4_NL
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 hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF:IQ4_NL # Run inference directly in the terminal: ./llama-cli -hf hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF:IQ4_NL
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 hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF:IQ4_NL # Run inference directly in the terminal: ./build/bin/llama-cli -hf hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF:IQ4_NL
Use Docker
docker model run hf.co/hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF:IQ4_NL
- LM Studio
- Jan
- vLLM
How to use hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF:IQ4_NL
- SGLang
How to use hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF with Ollama:
ollama run hf.co/hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF:IQ4_NL
- Unsloth Studio new
How to use hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF 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 hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF 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 hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF to start chatting
- Docker Model Runner
How to use hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF with Docker Model Runner:
docker model run hf.co/hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF:IQ4_NL
- Lemonade
How to use hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF:IQ4_NL
Run and chat with the model
lemonade run user.Phi-3.5-mini-instruct-IQ4_NL-GGUF-IQ4_NL
List all available models
lemonade list
hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF
This model was converted to GGUF format from microsoft/Phi-3.5-mini-instruct using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF --hf-file phi-3.5-mini-instruct-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF --hf-file phi-3.5-mini-instruct-iq4_nl-imat.gguf -c 2048
The Ship's Computer:
Interact with this model by speaking to it. Lean, fast, & private, networked speech to text, AI images, multi-modal voice chat, control apps, webcam, and sound with less than 4GiB of VRAM.
git clone -b main --single-branch https://github.com/themanyone/whisper_dictation.git
pip install -r whisper_dictation/requirements.txt
git clone https://github.com/ggerganov/whisper.cpp
cd whisper.cpp
GGML_CUDA=1 make -j # assuming CUDA is available. see docs
ln -s server ~/.local/bin/whisper_cpp_server # (just put it somewhere in $PATH)
whisper_cpp_server -l en -m models/ggml-tiny.en.bin --port 7777
cd whisper_dictation
./whisper_cpp_client.py
See the docs for tips on integrating with llama.cpp server, enabling the computer to talk back, draw AI images, carry out voice commands, and other features.
Install Llama.cpp via git:
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF --hf-file phi-3.5-mini-instruct-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF --hf-file phi-3.5-mini-instruct-iq4_nl-imat.gguf -c 2048
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Model tree for hellork/Phi-3.5-mini-instruct-IQ4_NL-GGUF
Base model
microsoft/Phi-3.5-mini-instruct