SmolLM2-360M-Instruct_Resume-SFT-DPO/README.md

license, language, library_name, tags, base_model, datasets, pipeline_tag

license	language	library_name	tags	base_model	datasets	pipeline_tag
apache-2.0	en	transformers	smollm2 onnx transformers.js text-generation fine-tuned sft dpo lora resume chatbot	HuggingFaceTB/SmolLM2-360M-Instruct	justinthelaw/Resume-DPO-SFT-Dataset	text-generation

justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO

A fine-tuned version of HuggingFaceTB/SmolLM2-360M-Instruct trained with a two-stage pipeline (SFT + DPO) 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 a two-stage approach optimized for factual memorization:

1. SFT (Supervised Fine-Tuning): Primary training for factual memorization using conversation-formatted QA pairs
2. DPO (Direct Preference Optimization): Refinement training to prefer accurate answers over hallucinations

Training Details

• Base Model: HuggingFaceTB/SmolLM2-360M-Instruct
• Training Dataset: justinthelaw/Resume-DPO-SFT-Dataset
• LoRA Configuration:
  • Rank (r): 32
  • Alpha: 64
  • Dropout: 0.05
  • Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

SFT Training Configuration

• Epochs: 5
• Batch Size: 8
• Learning Rate: 0.0001

DPO Training Configuration

• Epochs: 1
• Batch Size: 4
• Learning Rate: 5e-06
• Beta: 0.05
• Loss Type: sigmoid

Model Formats

This repository contains multiple model formats:

Format	Location	Use Case
SafeTensors	/ (root)	Python/PyTorch inference
ONNX	/onnx/model.onnx	Full precision ONNX Runtime
ONNX Quantized	/onnx/model_quantized.onnx	Browser inference (transformers.js)

Note

: If quantization fails during export due to weight distribution issues, model_quantized.onnx will be a copy of the fp16 model for compatibility.

Usage

Browser (transformers.js)

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

const generator = await pipeline(
  "text-generation",
  "justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO",
  { dtype: "q8" } // Uses model_quantized.onnx
);

const output = await generator("What is Justin's background?", {
  max_new_tokens: 256,
  temperature: 0.7,
});

Python (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO")
tokenizer = AutoTokenizer.from_pretrained("justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO")

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

Author

Justin

• GitHub: justinthelaw
• HuggingFace: justinthelaw

License

This model is released under the Apache 2.0 license.