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

Name: SmolLM2-360M-Instruct_Resume-SFT-DPO
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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:

SFT (Supervised Fine-Tuning): Primary training for factual memorization using conversation-formatted QA pairs
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)

javascript

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)

python

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