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README.md

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+
+---
+language:
+- en
+license: apache-2.0
+tags:
+- text-generation
+- instruct
+- manim
+- lora
+- gguf
+datasets:
+- ArunKr/verified-data-manim
+base_model: HuggingFaceTB/SmolLM-135M-Instruct
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# gemma-3-270m-it-web-agent - Fine-tuned
+
+This repository contains three variants of the model:
+
+- **LoRA adapters** → [ArunKr/gemma-3-270m-it-web-agent-lora](https://huggingface.co/ArunKr/gemma-3-270m-it-web-agent-lora)
+- **Merged FP16 weights** → [ArunKr/gemma-3-270m-it-web-agent-16bit](https://huggingface.co/ArunKr/gemma-3-270m-it-web-agent-16bit)
+- **GGUF quantizations** → [ArunKr/gemma-3-270m-it-web-agent-gguf](https://huggingface.co/ArunKr/gemma-3-270m-it-web-agent-gguf)
+
+### Training
+- Base model: `unsloth/gemma-3-270m-it`
+- Dataset: `ArunKr/gui_grounding_dataset-100`
+- Method: LoRA fine-tuning with [Unsloth](https://github.com/unslothai/unsloth)
+
+### Quantizations
+We provide `f16`, `bf16`, `f32`, and `q8_0` GGUF files for llama.cpp / Ollama.
+
+### Usage Example
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tok = AutoTokenizer.from_pretrained("ArunKr/gemma-3-270m-it-web-agent-16bit")
+model = AutoModelForCausalLM.from_pretrained("ArunKr/gemma-3-270m-it-web-agent-16bit")
+print(model.generate(**tok("Hello", return_tensors="pt")))
+```
+
+### Ollama Example
+
+```bash
+ollama run ArunKr/SmolLM-135M-Instruct-manim-gguf:<file_name>.gguf
+```
+
+www.ollama.com