Instructions to use justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO with Transformers:

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

pipe = pipeline("text-generation", model="justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO")
model = AutoModelForCausalLM.from_pretrained("justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Transformers.js

How to use justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO with Transformers.js:

// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';

// Allocate pipeline
const pipe = await pipeline('text-generation', 'justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO');

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-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": "justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO

SGLang

How to use justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-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 "justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-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": "justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-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 "justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-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": "justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO with Docker Model Runner:
```
docker model run hf.co/justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO
```

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)

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.

Downloads last month: 10

Safetensors

Model size

0.4B params

Tensor type

F16

Model tree for justinthelaw/SmolLM2-360M-Instruct_Resume-SFT-DPO

Base model

HuggingFaceTB/SmolLM2-360M

Quantized

HuggingFaceTB/SmolLM2-360M-Instruct

Adapter

(38)

this model

justinthelaw
/

SmolLM2-360M-Instruct_Resume-SFT-DPO