Instructions to use johnsoupir/Shiny-Phi3.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use johnsoupir/Shiny-Phi3.5 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="johnsoupir/Shiny-Phi3.5",
	filename="GGUF/Shiny-Phi3.5.F16..gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use johnsoupir/Shiny-Phi3.5 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 johnsoupir/Shiny-Phi3.5:F16
# Run inference directly in the terminal:
llama cli -hf johnsoupir/Shiny-Phi3.5:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf johnsoupir/Shiny-Phi3.5:F16
# Run inference directly in the terminal:
llama cli -hf johnsoupir/Shiny-Phi3.5:F16

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 johnsoupir/Shiny-Phi3.5:F16
# Run inference directly in the terminal:
./llama-cli -hf johnsoupir/Shiny-Phi3.5:F16

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 johnsoupir/Shiny-Phi3.5:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf johnsoupir/Shiny-Phi3.5:F16

Use Docker

docker model run hf.co/johnsoupir/Shiny-Phi3.5:F16

LM Studio
Jan
Ollama
How to use johnsoupir/Shiny-Phi3.5 with Ollama:
```
ollama run hf.co/johnsoupir/Shiny-Phi3.5:F16
```

Unsloth Studio

How to use johnsoupir/Shiny-Phi3.5 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 johnsoupir/Shiny-Phi3.5 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 johnsoupir/Shiny-Phi3.5 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for johnsoupir/Shiny-Phi3.5 to start chatting

Atomic Chat new
Docker Model Runner
How to use johnsoupir/Shiny-Phi3.5 with Docker Model Runner:
```
docker model run hf.co/johnsoupir/Shiny-Phi3.5:F16
```

Lemonade

How to use johnsoupir/Shiny-Phi3.5 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull johnsoupir/Shiny-Phi3.5:F16

Run and chat with the model

lemonade run user.Shiny-Phi3.5-F16

List all available models

lemonade list

Shiny-Phi3.5

Shiny-Phi3.5 is a reflection fine-tune of Phi3.5 using mahiatlinux's dataset.

Recently "Reflection 70B" drew a lot of attention after making claims of massive performance gains via reflection tuning. However, independent testing has been unable to reproduce these results.

I was curious to try it myself, so I made this model. If you'd like to try a smaller reflection model for yourself, or just one that's not associated with the original - then here you go!

What is reflection? Reflection fine-tuning guides the model to generate a plan, and then reflect on the plan before proceeding to the final output. A similar approach has been used by Claude: instructing the model to plan and reflect via system prompts. Reflection tuning "bakes in" the behavior.

Downloads last month: 16

GGUF

Model size

4B params

Architecture

llama

Hardware compatibility

4-bit

16-bit

View +1 variant

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for johnsoupir/Shiny-Phi3.5

Base model

microsoft/Phi-3.5-mini-instruct

Quantized

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Dataset used to train johnsoupir/Shiny-Phi3.5