Instructions to use zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF 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 zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF:Q4_0 # Run inference directly in the terminal: llama cli -hf zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF:Q4_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF:Q4_0 # Run inference directly in the terminal: llama cli -hf zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF:Q4_0
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 zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF:Q4_0 # Run inference directly in the terminal: ./llama-cli -hf zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF:Q4_0
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 zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF:Q4_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF:Q4_0
Use Docker
docker model run hf.co/zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF:Q4_0
- LM Studio
- Jan
- vLLM
How to use zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zsolx2/Phi-3.5-mini-instruct-Q4_0-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": "zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF:Q4_0
- SGLang
How to use zsolx2/Phi-3.5-mini-instruct-Q4_0-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 "zsolx2/Phi-3.5-mini-instruct-Q4_0-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": "zsolx2/Phi-3.5-mini-instruct-Q4_0-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 "zsolx2/Phi-3.5-mini-instruct-Q4_0-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": "zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF with Ollama:
ollama run hf.co/zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF:Q4_0
- Unsloth Desktop
- Docker Model Runner
How to use zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF with Docker Model Runner:
docker model run hf.co/zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF:Q4_0
- Lemonade
How to use zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF:Q4_0
Run and chat with the model
lemonade run user.Phi-3.5-mini-instruct-Q4_0-GGUF-Q4_0
List all available models
lemonade list
- Atomic Chat
Download phi-3.5-mini-instruct-q4_0.gguf from zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 2.18 GB
-
https://huggingface.co/zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF/resolve/main/phi-3.5-mini-instruct-q4_0.gguf
- Command line
-
hf download hf://zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF/phi-3.5-mini-instruct-q4_0.gguf
-
curl -L -o phi-3.5-mini-instruct-q4_0.gguf https://huggingface.co/zsolx2/Phi-3.5-mini-instruct-Q4_0-GGUF/resolve/main/phi-3.5-mini-instruct-q4_0.gguf
2.18 GB
- Xet hash:
- 9bb30e42d83d8b42546a75afc9f44e577a3219daf88b7d3543e0675596abf7f5
- Size of remote file:
- 2.18 GB
- SHA256:
- b5374915da534cb93df39f03bd4f2cd5a0c533df0d5e21957dc9556c260be9eb
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