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vishnuamarapu/
Full-Fine-Tuning-Qwen-2.5-0.5B-instruct-sft-prefix-tuned

Name: Full-Fine-Tuning-Qwen-2.5-0.5B-instruct-sft-prefix-tuned
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Full-Fine-Tuning-Qwen-2.5-0.5B-instruct-sft-prefix-tuned

Overview

This repository contains a Prefix Tuning (PEFT) adapter trained on top of my Supervised Fine-Tuned (SFT) personalized Qwen model.

Instead of updating all model weights, Prefix Tuning learns a small set of trainable prefix key/value vectors while keeping the underlying language model completely frozen. This significantly reduces the number of trainable parameters while retaining the personalized knowledge learned during supervised fine-tuning.

Note: This repository contains only the Prefix Tuning adapter, not the complete language model. To use this adapter, you must first load the corresponding SFT model.

Base Model

This Prefix Tuning adapter was trained on the following model:

BASE_MODEL = "vishnuamarapu/Full-Fine-Tuning-Qwen-2.5-0.5B-instruct-sft"

Do not load the original Qwen/Qwen2.5-0.5B-Instruct model directly, as the adapter was trained specifically on the personalized SFT model.

Installation

pip install -U transformers peft accelerate torch

Loading the Prefix Tuning Adapter

python

import torch

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

BASE_MODEL = "vishnuamarapu/Full-Fine-Tuning-Qwen-2.5-0.5B-instruct-sft"

PREFIX_ADAPTER = (
    "vishnuamarapu/"
    "Full-Fine-Tuning-Qwen-2.5-0.5B-instruct-sft-prefix-tuned"
)

tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)

model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    device_map="auto",
    torch_dtype="auto"
)

model = PeftModel.from_pretrained(
    model,
    PREFIX_ADAPTER
)

Example Inference

python

messages = [
    {
        "role": "system",
        "content": (
            "You are Vishnu's personal AI assistant. "
            "Answer questions about Vishnu."
        )
    },
    {
        "role": "user",
        "content": "What is your name?"
    }
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

inputs = tokenizer(
    text,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=80,
    do_sample=False
)

response = tokenizer.decode(
    outputs[0],
    skip_special_tokens=True
)

print(response)

Training Method

Base Architecture: Qwen2.5-0.5B-Instruct
Base Personalized Model: Full Supervised Fine-Tuning (SFT)
PEFT Method: Prefix Tuning
Backbone Parameters: Frozen
Trainable Parameters: Prefix Key/Value Vectors Only
Task: Personalized Question Answering

Repository Contents

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

The repository stores only the Prefix Tuning adapter, making it lightweight and efficient to distribute.

Intended Use

This adapter is intended for:

Personalized AI assistants
Parameter-Efficient Fine-Tuning (PEFT) research
Prefix Tuning experiments
Educational and learning purposes
Benchmarking against other PEFT techniques such as Prompt Tuning, LoRA, and QLoRA

Notes

This adapter must be used together with the SFT model listed above.
It was trained specifically for answering questions about Vishnu Amarapu.
The adapter is lightweight and contains only the learned Prefix Tuning parameters, making it much smaller than a fully fine-tuned model.

License

Please follow the license terms of the underlying Qwen model when using this adapter.

vishnuamarapu/Full-Fine-Tuning-Qwen-2.5-0.5B-instruct-sft-prefix-tuned