Update README.md

README.md

@@ -1,8 +1,50 @@
 ---
 license: mit
 language:
-- en
+  - en
 base_model:
-- mistralai/Mistral-7B-v0.1
+  - mistralai/Mistral-7B-v0.1
 pipeline_tag: text-generation
----
+tags:
+  - academic-writing
+  - mistral
+  - qlora
+  - fine-tuning
+  - arxiv
+  - llm
+  - uc-davis
+---
+
+# Mistral-7B Fine-Tuned for Academic Style (QLoRA)
+
+This is a parameter-efficient fine-tuning of `mistralai/Mistral-7B-v0.1` using QLoRA on 500K academic abstracts. It was built for the ECS 271 final project at UC Davis.
+
+## Intended Use
+
+The model is designed to generate formal academic paragraphs given a paper title, useful for research drafts, educational AI tools, and academic-style assistants.
+
+## Training Details
+
+- Base model: `mistralai/Mistral-7B-v0.1`
+- Method: QLoRA (low-rank adapter)
+- Prompt format: "Write an academic paragraph given the title: ..."
+- Dataset: 500K arXiv abstracts
+- Epochs: 1
+- GPU: RTX 5070 Ti (~60 hours)
+
+## Limitations
+
+- Generic or templated outputs
+- No citation support
+- Frequent hallucinations
+
+## Example
+
+**Prompt:**  
+"Write an academic paragraph given the title: LoRA for In-Context Learning"
+
+**Output:**  
+"We present LoRA (Loosely Regularized Adapters), a novel approach to fine-tune large language models for in-context learning tasks. Unlike traditional methods, LoRA updates only a small number of trainable parameters, achieving comparable performance while reducing training costs."
+
+## More Info
+Main Project: [Github](https://github.com/Joshua-Sun-CompSci/academic-style-llms)