Add README

README.md

@@ -3,122 +3,18 @@ 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`.
-
-## 📌 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.
+# SmolLM2-360M-Assignment-Metadata-Extractor : GGUF
 
----
+This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
 
- This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
+**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`
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](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)