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README.md

@@ -1,58 +1,37 @@
 ---
 base_model: google/functiongemma-270m-it
-library_name: transformers
-model_name: mobile-actions-functiongemma
 tags:
-- generated_from_trainer
-- trl
-- sft
-licence: license
+- function-calling
+- mobile-actions
+- gemma
+- fine-tuned
+license: gemma
+language:
+- en
 ---
 
-# Model Card for mobile-actions-functiongemma
+# FunctionGemma 270M - Mobile Actions
 
-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).
+Fine-tuned model based on `google/functiongemma-270m-it` for mobile action function calling.
 
-## Quick start
-
-```python
-from transformers import pipeline
-
-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"])
-```
-
-## Training procedure
-
- 
+## Functions
 
+This model can call the following functions:
+- `change_background_color`: Changes app background color
+- `search_web`: Searches the web for information
+- `set_timer`: Sets a timer for specified duration
 
-This model was trained with SFT.
-
-### Framework versions
-
-- TRL: 0.25.1
-- Transformers: 4.57.1
-- Pytorch: 2.9.0+cu126
-- Datasets: 4.4.1
-- Tokenizers: 0.22.1
-
-## Citations
+## Training
 
+- **Base Model**: google/functiongemma-270m-it
+- **Training Data**: 30 custom examples (27 train, 3 eval)
+- **Method**: LoRA fine-tuning
+- **Epochs**: 2
 
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-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}}
-}
-```
+model = AutoModelForCausalLM.from_pretrained("owllwo98/functiongemma-270m-it-mobile-actions")
+tokenizer = AutoTokenizer.from_pretrained("owllwo98/functiongemma-270m-it-mobile-actions")
+```