README.md

---
tags:
- gguf
- llama.cpp
- unsloth
- smollm2
- json-extraction
- data-extraction
language:
- en
license: apache-2.0
base_model: HuggingFaceTB/SmolLM2-360M
---

# 🎓 SmolLM2-360M-Assignment-Metadata-Extractor (GGUF)

This is a highly specialized, lightweight (360M parameter) model fine-tuned specifically to extract student metadata from chaotic, noisy assignment text and output it as strictly formatted JSON. 

It was finetuned and converted to 4-bit GGUF format using [Unsloth](https://github.com/unslothai/unsloth) for maximum CPU/GPU efficiency and rapid deployment via Ollama or `llama.cpp`.

**Github Repo**: *https://github.com/nmdra/Assignment-Metadata-Extractor*

## 📌 Model Capabilities

Unlike generic LLMs, this model has been purposefully overfit on a highly mutated dataset to act as a **Zero-Shot Data Extractor**. It excels at:
- **Noise Filtering:** Completely ignoring conversational filler, apologies, word counts, formatting artifacts, and academic instructions.
- **Handling Chaos:** Robust against typos (e.g., "Stuednt No"), varied capitalization, and unpredictable line breaks.
- **Strict JSON Output:** Trained to output ONLY a valid JSON object with zero conversational preamble (no "Here is the JSON...").

### Expected Output Schema
The model will exclusively output data in the following JSON structure:
```json
{
  "student_number": "...",
  "student_name": "...",
  "assignment_number": "..."
}
````

-----

## 🚀 Deployment & Usage

Because this model was trained with a specific instruction template, it performs best when wrapped in an environment that enforces **Temperature 0** and matches the training prompt.

### Method 1: Using Ollama (Recommended for standard usage)

Create a `Modelfile` with the following configuration to enforce the correct prompt template and prevent creativity:

```text
FROM hf.co/nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M

TEMPLATE """### Instruction:
Extract student info as JSON from the following text.

### Input:
{{ .Prompt }}

### Response:
"""

SYSTEM """
You are a precise student assignment data extractor.
Output ONLY a valid JSON object. No explanation. No extra text. No markdown.
Always output exactly: {"student_number":"...","student_name":"...","assignment_number":"..."}
"""

PARAMETER temperature 0
PARAMETER stop "}"
```

**Build and Run:**

```bash
ollama create json-extractor -f Modelfile
ollama run json-extractor "Course: CS101 \n Stuednt No=20210088 \n Full Nme: Nimal Silva \n HW No.-03 \n Please grade fairly!"
```

### Method 2: Python using Outlines (For bulletproof JSON validation)

For production environments where `json.JSONDecodeError` is entirely unacceptable, use this model with `outlines` and `llama-cpp-python` to structurally constrain the output tokens.

```python
import outlines
from pydantic import BaseModel

class StudentExtraction(BaseModel):
    student_number: str
    student_name: str
    assignment_number: str

# Load the GGUF model
model = outlines.models.llamacpp(
    "hf.co/nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M",
    device="cpu" # or "cuda"
)

# Constrain the generator to the Pydantic schema
generator = outlines.generate.json(model, StudentExtraction)

# Format the prompt exactly as trained
prompt = (
    "### Instruction:\nExtract student info as JSON from the following text.\n\n"
    "### Input:\nStu. ID: 20210088 | Full Name: Nimal Silva | HW-3\n\n"
    "### Response:\n"
)

result = generator(prompt)
print(result.model_dump_json())
```

-----

## 🧠 Training Details

  - **Base Model:** `HuggingFaceTB/SmolLM2-360M`
  - **Dataset:** 1,250 highly varied synthetic examples containing realistic human errors, markdown noise, and distractor text.
  - **Epochs:** 5 (Optimized to achieve a loss \< \~0.40 to prevent hallucinations).
  - **Framework:** Trained efficiently using LoRA (Low-Rank Adaptation) via Unsloth.
  - **Quantization:** Exported to `Q4_K_M` GGUF format to reduce memory footprint to \~270MB.

---

 This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)