Update README.md

README.md

@@ -9,50 +9,16 @@ tags:
 licence: license
 ---
 
-# Model Card for final_model_stable
+# Model Card for functiongemma-smarthome
 
-This model is a fine-tuned version of [google/functiongemma-270m-it](https://huggingface.co/google/functiongemma-270m-it).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+[Dataset](https://huggingface.co/datasets/altaidevorg/smarthome-tool-calling-tiny) | [Notebook](https://github.com/altaidevorg/functiongemma-afterimage-demo/blob/main/fgemma-training.ipynb) | [Demo Video](https://www.youtube.com/watch?v=TJxtyrWSgo0)
 
-## Quick start
+This model is a fine-tuned version of [google/functiongemma-270m-it](https://huggingface.co/google/functiongemma-270m-it) on a [custom tool-calling dataset](https://huggingface.co/datasets/altaidevorg/smarthome-tool-calling-tiny) synthetically generated with Afterimage, our purpose-built synthetic dataset generation engine.
 
-```python
-from transformers import pipeline
+See the [demo video](https://www.youtube.com/watch?v=TJxtyrWSgo0).
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="None", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
+## What is Afterimage?
+Building custom Small Language Models (SLMs) starts with great data. Afterimage eliminates the tedious data preparation bottleneck by transforming your organization's unstructured documents into high-quality, LLM-ready Q&A sets, tool-calling datasets and/or other types  of structured datasets automatically. It is highly customizable and and aimed at transforming enterprises' way of customizing LLMs.
 
-## Training procedure
-
- 
-
-
-This model was trained with SFT.
-
-### Framework versions
-
-- TRL: 0.23.0
-- Transformers: 4.56.2
-- Pytorch: 2.8.0
-- Datasets: 4.4.2
-- Tokenizers: 0.22.1
-
-## Citations
-
-
-
-Cite TRL as:
-    
-```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
-```
+## About ALTAI
+ALTAI is a secure, no-code platform that enables organizations to create, train, and deploy customized SLMs using their own internal documents. From "Letsearch" (RAG Engine) to on-premise deployment, we make LLM customization uncool again—simply effective. It can work 100% on-premise and requires 0 technical experience.