SmolLM2-360M-Instruct-Text-2-JSON/README.md

---
license: apache-2.0
language:
- en
metrics:
- accuracy
- perplexity
base_model:
- unsloth/SmolLM2-360M-Instruct-bnb-4bit
tags:
- transformers
- unsloth
- trl
- llama
- text-to-json
- Text-2-JSON
- Text-To-JSON
- calendar-parsing
- entity-extraction
- json-structured-output
- event-scheduling
- 4-bit
library_name: transformers
---

Developed by: [Pramod Koujalagi](https://www.pramodkoujalagi.com)

# SmolLM2-360M-Instruct-Text-2-JSON
A fine-tuned version of SmolLM2-360M-Instruct-bnb-4bit specialized for parsing unstructured calendar event requests into structured JSON data.

## Model Description
This model is fine-tuned on SmolLM2-360M-Instruct-bnb-4bit using QLoRA to extract structured calendar event information from natural language text. It identifies and structures key scheduling entities like action, date, time, attendees, location, duration, recurrence, and notes.


### 📦 Example Usage

You can use the `SmolLM2-360M-Instruct-Text-2-JSON` model to parse natural language event descriptions into structured JSON format.

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import json

# Load model and tokenizer
model_name = "pramodkoujalagi/SmolLM2-360M-Instruct-Text-2-JSON"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def parse_calendar_event(text):
    # Format the prompt
    formatted_prompt = f"""<|im_start|>user
Extract the relevant event information from this text and organize it into a JSON structure with fields for action, date, time, attendees, location, duration, recurrence, and notes. If a field is not present, return null for that field.

Text: {text}
<|im_end|>
<|im_start|>assistant
"""

    # Generate response
    inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device)
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=512,
            do_sample=True,
            temperature=0.1,
            top_p=0.95,
            pad_token_id=tokenizer.eos_token_id
        )

    # Process response
    output_text = tokenizer.decode(outputs[0], skip_special_tokens=False)
    response = output_text.split("<|im_start|>assistant\n")[1].split("<|im_end|>")[0].strip()

    # Return formatted JSON
    parsed_json = json.loads(response)
    return json.dumps(parsed_json, indent=2)

# Example input
event_text = "Plan an exhibition walkthrough on 15th, April 2028 at 3 PM with Harper, Grace, and Alex in the art gallery for 1 hour, bring bag."

# Output
print("Prompt:")
print(event_text)
print("\nModel Output:")
print(parse_calendar_event(event_text))
```
Output
```
Prompt:
Plan an exhibition walkthrough on 15th, April 2028 at 3 PM with Harper, Grace, and Alex in the art gallery for 1 hour, bring bag.

Model Output:
{
  "action": "Plan an exhibition walkthrough",
  "date": "15/04/2028",
  "time": "3:00 PM",
  "attendees": [
    "Harper",
    "Grace",
    "Alex"
  ],
  "location": "art gallery",
  "duration": "1 hour",
  "recurrence": null,
  "notes": "Bring bag"
}
```
<!-- 

**Example:**
```
Input: "Plan an exhibition walkthrough on 15th, April 2028 at 3 PM with Harper, Grace, and Alex in the art gallery for 1 hour, bring bag."
```
```
Output: {
  "action": "Plan an exhibition walkthrough",
  "date": "15/04/2028",
  "time": "3:00 PM",
  "attendees": [
    "Harper",
    "Grace",
    "Alex"
  ],
  "location": "art gallery",
  "duration": "1 hour",
  "recurrence": null,
  "notes": Bring bag
}
``` -->

### Resources for more information:
- GitHub Repository: [SmolLM2-360M-Instruct-Text-2-JSON](https://github.com/pramodkoujalagi/SmolLM2-360M-Instruct-Text-2-JSON)
- Base Model: [SmolLM2-360M-Instruct-bnb-4bit](https://huggingface.co/unsloth/SmolLM2-360M-Instruct-bnb-4bit) (Derived from HuggingfaceTB/SmolLM2-360)

## Use Cases
- Calendar application integration
- Personal assistant scheduling systems
- Meeting summarization tools
- Email processing for event extraction

## Training Details

### Training Data
The model was trained on a custom dataset consisting of 1,149 examples (1,034 training, 115 validation) of natural language event descriptions paired with structured JSON outputs. The dataset includes a wide variety of event types, date/time formats, and varying combinations of fields.

### Training Procedure
- **Fine-tuning method**: QLoRA (Quantized Low-Rank Adaptation)
- **LoRA configuration**:
  - Rank: 64
  - Alpha: 32
  - Target modules: All key model components
  - Rank-stabilized LoRA: Enabled
- **Training hyperparameters**:
  - Batch size: 8 (2 per device × 4 gradient accumulation steps)
  - Learning rate: 2e-4 with cosine scheduler
  - Epochs: 3
  - Weight decay: 0.01
  - Optimizer: AdamW (8-bit)
  - Gradient checkpointing: Enabled
- **Training time**: ~15 minutes
- **Hardware used**: [T4 GPU](https://www.nvidia.com/en-in/data-center/tesla-t4/)

### Training Metrics
- Final training loss: 
- Final validation loss: 
- Validation perplexity: 1.2091