Instructions to use roger33303/phi3.5-4b-Instruct-Finetune-website-QnA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use roger33303/phi3.5-4b-Instruct-Finetune-website-QnA with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="roger33303/phi3.5-4b-Instruct-Finetune-website-QnA")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("roger33303/phi3.5-4b-Instruct-Finetune-website-QnA")
model = AutoModelForCausalLM.from_pretrained("roger33303/phi3.5-4b-Instruct-Finetune-website-QnA")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use roger33303/phi3.5-4b-Instruct-Finetune-website-QnA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "roger33303/phi3.5-4b-Instruct-Finetune-website-QnA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "roger33303/phi3.5-4b-Instruct-Finetune-website-QnA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/roger33303/phi3.5-4b-Instruct-Finetune-website-QnA

SGLang

How to use roger33303/phi3.5-4b-Instruct-Finetune-website-QnA 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 "roger33303/phi3.5-4b-Instruct-Finetune-website-QnA" \
    --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": "roger33303/phi3.5-4b-Instruct-Finetune-website-QnA",
		"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 "roger33303/phi3.5-4b-Instruct-Finetune-website-QnA" \
        --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": "roger33303/phi3.5-4b-Instruct-Finetune-website-QnA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use roger33303/phi3.5-4b-Instruct-Finetune-website-QnA 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 roger33303/phi3.5-4b-Instruct-Finetune-website-QnA 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 roger33303/phi3.5-4b-Instruct-Finetune-website-QnA to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for roger33303/phi3.5-4b-Instruct-Finetune-website-QnA to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="roger33303/phi3.5-4b-Instruct-Finetune-website-QnA",
    max_seq_length=2048,
)

Docker Model Runner
How to use roger33303/phi3.5-4b-Instruct-Finetune-website-QnA with Docker Model Runner:
```
docker model run hf.co/roger33303/phi3.5-4b-Instruct-Finetune-website-QnA
```

Phi-3.5B Finetuned Model

1. Introduction

This model is a finetuned version of the Phi-3.5B large language model. It is designed to provide detailed and accurate responses for university course-related queries. The model has been optimized to deliver insights into course details, fee structures, duration options, and campus locations, along with links to relevant course pages. The finetuning process utilized a domain-specific dataset to ensure precision and reliability.

2. Dataset Used for Finetuning

The finetuning of the Phi-3.5B model was performed using a private dataset created through web scraping. The data was collected from the University of Westminster website and included:

Course titles
Campus details
Duration options (full-time, part-time, distance learning)
Fee structures (for UK and international students)
Course descriptions
Direct links to course pages

The dataset was cleaned and structured to enhance the model's ability to generate accurate and context-aware responses.

3. How to Use This Model

To use the Phi-3.5B finetuned model, follow the steps below:

from transformers import TextStreamer

def chatml(question, model):
         messages = [{"role": "user", "content": question},]

         inputs = tokenizer.apply_chat_template(messages,
                                                tokenize=True,
                                                add_generation_prompt=True,
                                                return_tensors="pt",).to("cuda")

         text_streamer = TextStreamer(tokenizer, skip_special_tokens=True,
                                      skip_prompt=True)
         return model.generate(input_ids=inputs,
                               streamer=text_streamer,
                               max_new_tokens=512)
  
question = "Does the University of Westminster offer a course on AI, Data and Communication MA?"
x = query(question, model)

This setup ensures you can effectively query the Phi-3.5B finetuned model and receive detailed, relevant responses.

Uploaded model

Developed by: roger33303
License: apache-2.0
Finetuned from model : unsloth/phi-3.5-mini-instruct-bnb-4bit

Downloads last month: 3

Safetensors

Model size

4B params

Tensor type

BF16