Instructions to use Prithwiraj731/MotoData-Phi2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Prithwiraj731/MotoData-Phi2-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 Prithwiraj731/MotoData-Phi2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Prithwiraj731/MotoData-Phi2-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 Prithwiraj731/MotoData-Phi2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Prithwiraj731/MotoData-Phi2-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 Prithwiraj731/MotoData-Phi2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Prithwiraj731/MotoData-Phi2-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 Prithwiraj731/MotoData-Phi2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Prithwiraj731/MotoData-Phi2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Prithwiraj731/MotoData-Phi2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Prithwiraj731/MotoData-Phi2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Prithwiraj731/MotoData-Phi2-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Prithwiraj731/MotoData-Phi2-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Prithwiraj731/MotoData-Phi2-GGUF:Q4_K_M

Ollama
How to use Prithwiraj731/MotoData-Phi2-GGUF with Ollama:
```
ollama run hf.co/Prithwiraj731/MotoData-Phi2-GGUF:Q4_K_M
```

Unsloth Studio

How to use Prithwiraj731/MotoData-Phi2-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 Prithwiraj731/MotoData-Phi2-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 Prithwiraj731/MotoData-Phi2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Prithwiraj731/MotoData-Phi2-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use Prithwiraj731/MotoData-Phi2-GGUF with Docker Model Runner:
```
docker model run hf.co/Prithwiraj731/MotoData-Phi2-GGUF:Q4_K_M
```

Lemonade

How to use Prithwiraj731/MotoData-Phi2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Prithwiraj731/MotoData-Phi2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MotoData-Phi2-GGUF-Q4_K_M

List all available models

lemonade list

MotoData-Phi2-GGUF

This is a fine-tuned version of the microsoft/phi-2 model, trained on the Beluga6969/MotoData dataset.

This repository contains the quantized GGUF model file for easy use on local CPUs, as well as the original LoRA adapter for those who wish to build upon this work.

Model Details

Base Model: microsoft/phi-2
Dataset: Beluga6969/MotoData
Fine-tuning Method: QLoRA

Intended Use

This model is designed to be a helpful chatbot and question-answering assistant for motorcycle enthusiasts. It can answer questions about motorcycle maintenance, models, and general two-wheeler knowledge based on the data it was trained on.

Example Prompts:

"What should I check before a long motorcycle trip?"
"Tell me about the Royal Enfield Himalayan."
"How do you clean and lubricate a motorcycle chain?"

How to Use This GGUF Model

The primary file in this repository is phi2-custom-q4_k_m.gguf. You can run this file on your local computer (CPU or GPU) using tools like LM Studio, Ollama, or llama.cpp.

Using with LM Studio

Download and install LM Studio.
In the app, search for Prithwiraj731/MotoData-Phi2-GGUF.
Download the phi2-custom-q4_k_m.gguf file from the list.
Go to the Chat tab (💬 icon), select the model at the top, and start your conversation!

Using with Ollama

Download and install Ollama.

Create a file named Modelfile (without any extension) and paste the following content into it:

FROM ./phi2-custom-q4_k_m.gguf
TEMPLATE "<start_of_turn>user\n{{ .Prompt }}<end_of_turn>\n<start_of_turn>model\n"

Place this Modelfile in the same directory as the GGUF file you downloaded.

Open your terminal and run the command:

ollama create MotoDataPhi2 -f ./Modelfile

You can now chat with the model by running:
```
ollama run MotoDataPhi2
```

Using the LoRA Adapter

For advanced users, the fine_tuned_phi2_adapter folder is provided. You can merge this with the original microsoft/phi-2 model to create your own versions or continue fine-tuning.

Model fine-tuned by Prithwiraj731.

Downloads last month: 4

GGUF

Model size

3B params

Architecture

phi2

Hardware compatibility

4-bit