Instructions to use bartowski/Phi-3-medium-128k-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use bartowski/Phi-3-medium-128k-instruct-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 bartowski/Phi-3-medium-128k-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf bartowski/Phi-3-medium-128k-instruct-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf bartowski/Phi-3-medium-128k-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf bartowski/Phi-3-medium-128k-instruct-GGUF: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 bartowski/Phi-3-medium-128k-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bartowski/Phi-3-medium-128k-instruct-GGUF: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 bartowski/Phi-3-medium-128k-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bartowski/Phi-3-medium-128k-instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/bartowski/Phi-3-medium-128k-instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use bartowski/Phi-3-medium-128k-instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bartowski/Phi-3-medium-128k-instruct-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": "bartowski/Phi-3-medium-128k-instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bartowski/Phi-3-medium-128k-instruct-GGUF:Q4_K_M
- Ollama
How to use bartowski/Phi-3-medium-128k-instruct-GGUF with Ollama:
ollama run hf.co/bartowski/Phi-3-medium-128k-instruct-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use bartowski/Phi-3-medium-128k-instruct-GGUF with Docker Model Runner:
docker model run hf.co/bartowski/Phi-3-medium-128k-instruct-GGUF:Q4_K_M
- Lemonade
How to use bartowski/Phi-3-medium-128k-instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bartowski/Phi-3-medium-128k-instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Phi-3-medium-128k-instruct-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Correct promt format is crucial.
At first, when i tried this model with my usual reasoning questions, it failed a lot (wrong answers). And only when i used this >>> llama-cli -m Phi-3-medium-128k-instruct-Q4_K_M.gguf -p " " --in-prefix "<|user|>\n" --in-suffix "<|end|>\n<|assistant|>\n" , it suddenly became much smarter :)
I wish every model had correct settings for llama.cpp. Every time i struggle with it...
it should already be set to the right one
though for my curiousity, can you try tokenizing your prompt? the \n should get removed (which I think is a problem personally)
it should already be set to the right one
I'm not good at this. Maybe you could provide the correct settings / promt format for llama.cpp...
ollama modelfile:
FROM /your/path/Phi-3-medium-128k-instruct-Q8_0.gguf
PARAMETER stop "<|assistant|>"
PARAMETER stop "<|end|>"
PARAMETER stop "<|user|>"
PARAMETER temperature 0.3
TEMPLATE """{{ if .System }}<|system|>
{{ .System }}<|end|>
{{ end }}{{ if .Prompt }}<|user|>
{{ .Prompt }}<|end|>
{{ end }}<|assistant|>
{{ .Response }}<|end|>
"""