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README.md

@@ -1,58 +1,8 @@
 ---
-base_model: google/functiongemma-270m-it
-library_name: transformers
-model_name: functiongemma-270m-it-simple-tool-calling
+base_model: Oummadi/functiongemma-270m-it
 tags:
-- generated_from_trainer
-- sft
-- trl
-licence: license
+- function-calling
+- functiongemma-270m-it-simple-tool-calling
+- gemma
 ---
-
-# Model Card for functiongemma-270m-it-simple-tool-calling
-
-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).
-
-## 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="Oummadi/functiongemma-270m-it-simple-tool-calling", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
-
-## Training procedure
-
- 
-
-
-
-This model was trained with SFT.
-
-### Framework versions
-
-- TRL: 1.4.0
-- Transformers: 5.0.0
-- Pytorch: 2.10.0+cu128
-- Datasets: 4.8.5
-- Tokenizers: 0.22.2
-
-## Citations
-
-
-
-Cite TRL as:
-    
-```bibtex
-@software{vonwerra2020trl,
-  title   = {{TRL: Transformers Reinforcement Learning}},
-  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
-  license = {Apache-2.0},
-  url     = {https://github.com/huggingface/trl},
-  year    = {2020}
-}
-```
+A fine-tuned model based on `Oummadi/functiongemma-270m-it`.