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 Settings

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 / README.md

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	---
	base_model: Qwen/Qwen2.5-1.5B-Instruct
	library_name: peft
	pipeline_tag: text-generation
	license: mit
	tags:
	- base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct
	- lora
	- peft
	- transformers
	- text-generation
	- personalization
	---

	# 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

	```python
	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:

	```bash
	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:

	```bash
	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.