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justinthelaw/
Qwen2.5-0.5B-Instruct-Resume-Cover-Letter-SFT

Name: Qwen2.5-0.5B-Instruct-Resume-Cover-Letter-SFT
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justinthelaw/Qwen2.5-0.5B-Instruct-Resume-Cover-Letter-SFT

A fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct trained with an SFT + LoRA pipeline to answer questions about Justin's professional background, skills, and experience.

Model Description

This model is designed for browser-based inference using transformers.js. It powers a personal website chatbot that can answer questions about Justin's resume, work experience, education, and skills.

Training Pipeline

The model is trained using SFT (Supervised Fine-Tuning) with LoRA adapters, where factual memorization is enforced via conversation-formatted QA pairs.

Training Details

Base Model: Qwen/Qwen2.5-0.5B-Instruct
Training Dataset: justinthelaw/Resume-Cover-Letter-SFT-Dataset
LoRA Configuration:
- Rank (r): 64
- Alpha: 128
- Dropout: 0.05
- Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

SFT Training Configuration

Epochs: 8
Batch Size: 16
Learning Rate: 2e-05

Model Formats

This repository contains multiple model formats:

Format	Location	Use Case
SafeTensors	`/` (root)	Python/PyTorch inference
ONNX	`/onnx/`	FP32 + quantized weights for ONNX Runtime/Web inference

Usage

Browser (transformers.js)

javascript

import { pipeline } from "@huggingface/transformers";

const generator = await pipeline("text-generation", "justinthelaw/Qwen2.5-0.5B-Instruct-Resume-Cover-Letter-SFT", {
  dtype: "fp32",
});

const output = await generator("What is Justin's background?", {
  max_new_tokens: 256,
});

Python (Transformers)

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("justinthelaw/Qwen2.5-0.5B-Instruct-Resume-Cover-Letter-SFT")
tokenizer = AutoTokenizer.from_pretrained("justinthelaw/Qwen2.5-0.5B-Instruct-Resume-Cover-Letter-SFT")

prompt = "What is Justin's background?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Intended Use

This model is intended for:

Personal website chatbots
Resume Q&A applications
Demonstrating fine-tuning techniques for personalized AI assistants

Limitations

The model is specifically trained on Justin's resume and may not generalize to other topics
Responses are based on training data and may not reflect real-time information
Not suitable for general-purpose question answering