Update Readme.md

README.md

@@ -3,18 +3,122 @@ 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
+# 🎓 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`.
+
+## 📌 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":"..."}
+"""
 
-This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
+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.
+
+---
 
-**Example usage**:
-- For text only LLMs:    `llama-cli -hf nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor --jinja`
-- For multimodal models: `llama-mtmd-cli -hf nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor --jinja`
+ This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
 
-## Available Model files:
-- `SmolLM2-360M.Q4_K_M.gguf`
-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)
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)