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README.md

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+---
+language: en
+tags:
+  - autoscientist
+  - adaption-labs
+  - qlora
+  - legal
+  - fine-tuned
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+datasets:
+  - Rishidar/autoscientist-competition-datasets
+---
+
+# AutoScientist Competition — Legal Model
+
+Fine-tuned from [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on
+the [legal adapted dataset](https://huggingface.co/datasets/Rishidar/autoscientist-competition-datasets)
+produced by [Adaption Labs](https://adaptionlabs.ai) AutoScientist Challenge.
+
+## Training
+- Method: QLoRA (4-bit NF4, r=32, alpha=64)
+- Epochs: 3
+- Learning rate: 0.0002
+- Dataset quality: Grade A (Adaption Labs evaluation)
+
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("Rishidar/autoscientist-legal-qlora")
+tokenizer = AutoTokenizer.from_pretrained("Rishidar/autoscientist-legal-qlora")
+```