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README.md
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base_model: unsloth/gemma-3-270m-it
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- gemma3_text
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license: apache-2.0
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language:
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- en
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/gemma-3-270m-it
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language:
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- en
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license: apache-2.0
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tags:
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- text-generation
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- instruct
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- manim
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- lora
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- gguf
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datasets:
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- ArunKr/verified-data-manim
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base_model: HuggingFaceTB/SmolLM-135M-Instruct
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library_name: transformers
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pipeline_tag: text-generation
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# gemma-3-270m-it-web-agent - Fine-tuned
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This repository contains three variants of the model:
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- **LoRA adapters** → [ArunKr/gemma-3-270m-it-web-agent-lora](https://huggingface.co/ArunKr/gemma-3-270m-it-web-agent-lora)
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- **Merged FP16 weights** → [ArunKr/gemma-3-270m-it-web-agent-16bit](https://huggingface.co/ArunKr/gemma-3-270m-it-web-agent-16bit)
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- **GGUF quantizations** → [ArunKr/gemma-3-270m-it-web-agent-gguf](https://huggingface.co/ArunKr/gemma-3-270m-it-web-agent-gguf)
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### Training
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- Base model: `unsloth/gemma-3-270m-it`
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- Dataset: `ArunKr/gui_grounding_dataset-100`
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- Method: LoRA fine-tuning with [Unsloth](https://github.com/unslothai/unsloth)
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### Quantizations
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We provide `f16`, `bf16`, `f32`, and `q8_0` GGUF files for llama.cpp / Ollama.
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### Usage Example
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tok = AutoTokenizer.from_pretrained("ArunKr/gemma-3-270m-it-web-agent-16bit")
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model = AutoModelForCausalLM.from_pretrained("ArunKr/gemma-3-270m-it-web-agent-16bit")
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print(model.generate(**tok("Hello", return_tensors="pt")))
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```
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### Ollama Example
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```bash
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ollama run ArunKr/SmolLM-135M-Instruct-manim-gguf:<file_name>.gguf
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```
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www.ollama.com
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