Instructions to use ceshim/personalized-qwen-dpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ceshim/personalized-qwen-dpo 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, "ceshim/personalized-qwen-dpo")

Transformers

How to use ceshim/personalized-qwen-dpo with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ceshim/personalized-qwen-dpo", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ceshim/personalized-qwen-dpo with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ceshim/personalized-qwen-dpo

SGLang

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

Docker Model Runner
How to use ceshim/personalized-qwen-dpo with Docker Model Runner:
```
docker model run hf.co/ceshim/personalized-qwen-dpo
```

Model Card for Model ID

A personalized version of Qwen2.5-1.5B-Instruct fine-tuned using Direct Preference Optimization (DPO) and LoRA to better align responses with individual user preferences.

Model Details

Model Description

This model is a personalized adaptation of Qwen2.5-1.5B-Instruct, developed as part of Stanford CS 329X: Human-Centered NLP (Homework 1). It was fine-tuned using Direct Preference Optimization (DPO) with LoRA adapters on a manually annotated preference dataset consisting of 18 prompts and 378 pairwise preference examples.

The goal of fine-tuning was to encourage responses that better reflect my preferences for relevance, clarity, and helpfulness. Qualitative comparisons with the base model suggested that the personalized model sometimes addressed user intent more directly, although improvements were not consistent across all prompts.

Developed by: [Chloe Shim]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [Chloe Shim]
Model type: [Causal language model (instruction-tuned), personalized using DPO with LoRA adapters]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Finetuned from model [optional]: [Qwen/Qwen2.5-1.5B-Instruct]

Model Sources [optional]

Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

BibTeX:

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Glossary [optional]

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Model Card Authors [optional]

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Model Card Contact

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Framework versions

PEFT 0.21.1

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Model tree for ceshim/personalized-qwen-dpo

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Adapter

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this model

Paper for ceshim/personalized-qwen-dpo

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 62