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

Libraries

How to use justinthelaw/SmolLM2-360M-Instruct-Resume-Cover-Letter-SFT with Transformers:

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

pipe = pipeline("text-generation", model="justinthelaw/SmolLM2-360M-Instruct-Resume-Cover-Letter-SFT")
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-Cover-Letter-SFT")
model = AutoModelForCausalLM.from_pretrained("justinthelaw/SmolLM2-360M-Instruct-Resume-Cover-Letter-SFT", device_map="auto")
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-Cover-Letter-SFT 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-Cover-Letter-SFT');

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

docker model run hf.co/justinthelaw/SmolLM2-360M-Instruct-Resume-Cover-Letter-SFT

SGLang

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

Docker Model Runner
How to use justinthelaw/SmolLM2-360M-Instruct-Resume-Cover-Letter-SFT with Docker Model Runner:
```
docker model run hf.co/justinthelaw/SmolLM2-360M-Instruct-Resume-Cover-Letter-SFT
```

SmolLM2-360M-Instruct-Resume-Cover-Letter-SFT

File size: 3,613 Bytes

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

# justinthelaw/SmolLM2-360M-Instruct-Resume-Cover-Letter-SFT

A fine-tuned version of [HuggingFaceTB/SmolLM2-360M-Instruct](https://huggingface.co/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](https://huggingface.co/docs/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 **SFT (Supervised Fine-Tuning)** approach, where factual memorization is enforced via conversation-formatted QA pairs.

### Training Details

- **Base Model**: [HuggingFaceTB/SmolLM2-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct)
- **Training Dataset**: [justinthelaw/Resume-Cover-Letter-SFT-Dataset](https://huggingface.co/datasets/justinthelaw/Resume-Cover-Letter-SFT-Dataset)
- **LoRA Configuration**:
  - Rank (r): 128
  - Alpha: 256
  - Dropout: 0.05
  - Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

#### SFT Training Configuration

- Epochs: 16
- 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/`   | Full precision and quantized weights for the ONNX Runtime |

## Usage

### Browser (transformers.js)

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

const generator = await pipeline(
  "text-generation",
  "justinthelaw/SmolLM2-360M-Instruct-Resume-Cover-Letter-SFT",
  { 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-Cover-Letter-SFT")
tokenizer = AutoTokenizer.from_pretrained("justinthelaw/SmolLM2-360M-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

## Author

### Justin

- GitHub: [justinthelaw](https://github.com/justinthelaw)
- HuggingFace: [justinthelaw](https://huggingface.co/justinthelaw)

## License

This model is released under the Apache 2.0 license.