Create README.md

README.md

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+---
+license: apache-2.0
+base_model: unsloth/functiongemma-270m-it
+tags:
+- function-calling
+- tool-use
+- mobile-actions
+- gemma
+- unsloth
+datasets:
+- google/mobile-actions
+language:
+- en
+pipeline_tag: text-generation
+---
+
+## Model Card
+
+This model is fine-tuned version of [google/functiongemma-270m-it](https://huggingface.co/google/functiongemma-270m-it) model for mobile action function calling tasks. 
+
+## Intended Use
+
+Handles function-calling style mobile actions such as creating calendar events, sending emails, adding contacts, showing maps, managing Wi‑Fi, and toggling the flashlight, based on the `google/mobile-actions` dataset.
+
+## Model Details
+
+- **Base Model**: [unsloth/functiongemma-270m-it](https://huggingface.co/unsloth/functiongemma-270m-it) (Gemma 3 distilled by Unsloth)
+- **Fine-tuning**: SFT with LoRA (merged with `merge_and_unload`, now single checkpoint)
+- **Dataset**: [google/mobile-actions](https://huggingface.co/datasets/google/mobile-actions)
+- **Params**: ~270M (base) + LoRA merged into final weights
+
+## Quick Start
+
+```bash
+pip install torch transformers datasets accelerate huggingface_hub
+```
+
+```python
+import torch
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+
+model_id = "dousery/functiongemma-mobile-actions"  
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+    device_map="auto" if device == "cuda" else None,
+    trust_remote_code=True,
+).eval()
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+if device == "cpu":
+    model = model.to(device)
+
+dataset = load_dataset("google/mobile-actions", split="train")
+text = tokenizer.apply_chat_template(
+    dataset[0]["messages"][:2],
+    tools=dataset[0]["tools"],
+    tokenize=False,
+    add_generation_prompt=True,
+).removeprefix("<bos>")
+
+inputs = tokenizer(text, return_tensors="pt").to(device)
+with torch.no_grad():
+    _ = model.generate(
+        **inputs,
+        max_new_tokens=256,
+        streamer=TextStreamer(tokenizer, skip_prompt=True),
+        top_p=0.95,
+        top_k=64,
+        temperature=1.0,
+    )
+```
+
+## Training Summary
+
+- **Frameworks**: Unsloth + TRL, PyTorch 2.9.1, Transformers 4.57.3
+- **Steps**: 100 (SFT with LoRA, then merged)
+- **Effective Batch Size**: 8 (bs=4, grad accum=2)
+- **LR / Scheduler**: 2e-4, linear
+- **LoRA**: r=16, alpha=16, dropout=0, ~3.8M trainable params
+- **Seq Len**: 4096
+- **Hardware**: NVIDIA H100 80GB on Modal
+- **Final Train Loss**: 0.2408 | **Eval Loss**: ~0.0129
+
+## Limitations
+
+- Trained for only 100 steps; niche mobile-action domain.
+- Datetime formats can drift slightly.
+- Best on GPU for speed; CPU works but slower.
+
+## Citation
+
+```bibtex
+@misc{functiongemma-mobile-actions,
+  title={FunctionGemma Mobile Actions - Merged for Mobile Function Calling},
+  author={dousery},
+  year={2025},
+  howpublished={\url{https://huggingface.co/dousery/functiongemma-mobile-actions}}
+}
+```
+
+## License
+
+Apache-2.0 (inherits base model license).
+