Instructions to use sairamn/Phi3-Legal-Finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sairamn/Phi3-Legal-Finetuned with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sairamn/Phi3-Legal-Finetuned",
	filename="Phi-3.1-Legal_Finetuned.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 sairamn/Phi3-Legal-Finetuned with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sairamn/Phi3-Legal-Finetuned
# Run inference directly in the terminal:
llama-cli -hf sairamn/Phi3-Legal-Finetuned

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sairamn/Phi3-Legal-Finetuned
# Run inference directly in the terminal:
llama-cli -hf sairamn/Phi3-Legal-Finetuned

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 sairamn/Phi3-Legal-Finetuned
# Run inference directly in the terminal:
./llama-cli -hf sairamn/Phi3-Legal-Finetuned

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 sairamn/Phi3-Legal-Finetuned
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sairamn/Phi3-Legal-Finetuned

Use Docker

docker model run hf.co/sairamn/Phi3-Legal-Finetuned

LM Studio
Jan

vLLM

How to use sairamn/Phi3-Legal-Finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sairamn/Phi3-Legal-Finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sairamn/Phi3-Legal-Finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sairamn/Phi3-Legal-Finetuned

Ollama
How to use sairamn/Phi3-Legal-Finetuned with Ollama:
```
ollama run hf.co/sairamn/Phi3-Legal-Finetuned
```

Unsloth Studio new

How to use sairamn/Phi3-Legal-Finetuned 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 sairamn/Phi3-Legal-Finetuned 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 sairamn/Phi3-Legal-Finetuned to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use sairamn/Phi3-Legal-Finetuned with Docker Model Runner:
```
docker model run hf.co/sairamn/Phi3-Legal-Finetuned
```

Lemonade

How to use sairamn/Phi3-Legal-Finetuned with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sairamn/Phi3-Legal-Finetuned

Run and chat with the model

lemonade run user.Phi3-Legal-Finetuned-{{QUANT_TAG}}

List all available models

lemonade list

Phi3-Legal-Finetuned

This is a fine-tuned version of the Phi-3 Mini model for legal text generation tasks.

Model Details

Base Model: Microsoft Phi-3 Mini 128K
Fine-tuned On: Legal documents and summaries
Context Length: 128K tokens
License: MIT

Usage

You can load the model using Hugging Face Transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "sairamn/Phi3-Legal-Finetuned"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

Limitations

The model is not a substitute for professional legal advice.
May generate incorrect or biased information.

Acknowledgments

Based on Microsoft Phi-3 Mini.

Citation

If you use this model, please cite accordingly.

Downloads last month: 3

GGUF

Model size

4B params

Architecture

phi3

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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Base model

microsoft/Phi-3-mini-128k-instruct

Quantized

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Phi3-Legal-Finetuned