Instructions to use sainiswayam9/qwen-lora-chatbot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sainiswayam9/qwen-lora-chatbot with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B")
model = PeftModel.from_pretrained(base_model, "sainiswayam9/qwen-lora-chatbot")

Transformers

How to use sainiswayam9/qwen-lora-chatbot with Transformers:

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

pipe = pipeline("text-generation", model="sainiswayam9/qwen-lora-chatbot")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sainiswayam9/qwen-lora-chatbot", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sainiswayam9/qwen-lora-chatbot with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sainiswayam9/qwen-lora-chatbot"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sainiswayam9/qwen-lora-chatbot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sainiswayam9/qwen-lora-chatbot

SGLang

How to use sainiswayam9/qwen-lora-chatbot 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 "sainiswayam9/qwen-lora-chatbot" \
    --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": "sainiswayam9/qwen-lora-chatbot",
		"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 "sainiswayam9/qwen-lora-chatbot" \
        --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": "sainiswayam9/qwen-lora-chatbot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sainiswayam9/qwen-lora-chatbot with Docker Model Runner:
```
docker model run hf.co/sainiswayam9/qwen-lora-chatbot
```

Qwen LoRA Chatbot

A LoRA fine-tuned version of Qwen/Qwen2.5-0.5B trained on instruction-following data using PEFT and TRL.

Model Details

Developed By

Swayam Saini

Model Type

PEFT / LoRA Adapter for Causal Language Modeling

Base Model

Qwen/Qwen2.5-0.5B

Language

English

Frameworks

Transformers
PEFT
TRL

PEFT Version

0.19.1

Training Dataset

Dataset used:

yahma/alpaca-cleaned

The dataset contains instruction-response pairs designed to improve instruction-following behavior.

Fine-Tuning Configuration

LoRA Rank (r): 16
LoRA Alpha: 32
LoRA Dropout: 0.05
Precision: FP16
Trainer: TRL SFTTrainer

Model Architecture

This repository contains a LoRA adapter trained on top of:

Qwen/Qwen2.5-0.5B

The final chatbot is created by combining:

Base Model + LoRA Adapter

Files

adapter_model.safetensors
adapter_config.json
tokenizer.json
tokenizer_config.json
chat_template.jinja

Intended Use

This model can be used for:

Conversational AI
Chatbots
Question Answering
Educational demonstrations
LLM fine-tuning experiments

Limitations

This model may:

Hallucinate facts
Produce inaccurate information
Reflect biases present in training data

Outputs should be reviewed before use in production systems.

Deployment

The adapter can be loaded using:

from transformers import AutoModelForCausalLM
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-0.5B"
)

model = PeftModel.from_pretrained(
    base_model,
    "sainiswayam9/qwen-lora-chatbot"
)

Project Summary

This project demonstrates:

LoRA fine-tuning
Parameter-efficient training
Hugging Face Model Hub integration
Hugging Face Space deployment
End-to-end LLM workflow

Author