Instructions to use alchin2/lora-project with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alchin2/lora-project with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "alchin2/lora-project")

Transformers

How to use alchin2/lora-project with Transformers:

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

pipe = pipeline("text-generation", model="alchin2/lora-project")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("alchin2/lora-project", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use alchin2/lora-project with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alchin2/lora-project"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alchin2/lora-project",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alchin2/lora-project

SGLang

How to use alchin2/lora-project 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 "alchin2/lora-project" \
    --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": "alchin2/lora-project",
		"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 "alchin2/lora-project" \
        --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": "alchin2/lora-project",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alchin2/lora-project with Docker Model Runner:
```
docker model run hf.co/alchin2/lora-project
```

LoRA Project

This repository contains a PEFT LoRA adapter for personalized email-style generation. The adapter was trained from Qwen/Qwen2.5-1.5B-Instruct using project-specific style examples exported from the ACM personalization pipeline.

This is not a standalone full model. Load it together with the base model.

Base Model

Qwen/Qwen2.5-1.5B-Instruct

Intended Use

The adapter is intended for class project experimentation with personalized writing style generation. It can be used with the companion GitHub project to generate email-style responses from a selected user's profile and prompt.

Project repo: https://github.com/AryanAGit/LLM_Personalization---ACM-AI-Team4

Loading The Adapter

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = "Qwen/Qwen2.5-1.5B-Instruct"
adapter_id = "alchin2/lora-project"

tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(base_model)
model = PeftModel.from_pretrained(model, adapter_id)

Local App Usage

From the GitHub project directory, run:

python3 project.py generate \
  --history data/processed/user_email_history.json \
  --backend peft \
  --base-model Qwen/Qwen2.5-1.5B-Instruct \
  --adapter-path alchin2/lora-project \
  --prompt "Write an email asking Wendy to send the files by Wednesday."

Or start the local web app:

python3 run_app.py

Then open http://127.0.0.1:8787.

Evaluation

Recommended evaluation methods for this project:

Automatic metrics from the project evaluation command, including style distance, greeting/signoff matching, length ratio, word overlap, and profile copy risk.
LaMP-style personalization evaluation, comparing outputs with and without user profile context.
Blind LLM or human ranking across base model, RAG, LoRA, and RAG+LoRA outputs.

Limitations

The adapter is trained for style imitation experiments and may overfit small user profiles. Outputs should be checked for prompt faithfulness, fluency, and copying from training examples.

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Model tree for alchin2/lora-project

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Adapter

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