Update README.md

README.md

@@ -1,3 +1,96 @@
 ---
 license: llama2
+language:
+- en
+base_model:
+- liuhaotian/llava-v1.5-7b
+pipeline_tag: visual-question-answering
+tags:
+- tem
+- materials-science
+- multimodal
 ---
+
+# TEM-LLM
+
+TEM-LLM is a domain-specific multimodal large language model for 
+Transmission Electron Microscopy (TEM) image analysis, fine-tuned 
+from LLaVA-v1.5-7B using a difficulty-aware curriculum learning strategy.
+
+This model is associated with the following work (currently under review):
+
+> *AI-Assisted Materials Characterization: A Curriculum-Guided 
+> Multimodal Framework for Transmission Electron Microscopy*
+
+---
+
+## Model Details
+
+| | |
+|---|---|
+| **Base Model** | LLaVA-v1.5-7B (Vicuna-v1.5-7B) |
+| **Training Strategy** | Difficulty-Aware Curriculum Learning (4 stages) |
+| **Training Data** | ~216K QA pairs across 40K TEM images |
+| **Domain** | Transmission Electron Microscopy (TEM) |
+| **Modalities** | CTEM, HR-TEM, STEM, Diffraction |
+| **Fine-tuning Method** | LoRA (Low-Rank Adaptation) |
+| **License** | LLaMA 2 |
+
+---
+
+## Intended Use
+
+TEM-LLM is designed for:
+- **Image Classification**: Identifying TEM imaging modalities 
+  (CTEM, HRTEM, STEM, diffraction)
+- **Microstructural Feature Detection**: Localizing and counting 
+  structural elements such as nanoparticles and grain boundaries
+- **Feature Analysis & Interpretation**: Providing scientific 
+  explanations of observed microstructural phenomena
+- **Comprehensive Image Description**: Integrating multiple aspects 
+  of visual understanding into structured descriptions
+
+---
+
+## Important: Inference Requirements
+
+TEM-LLM is built on LLaVA and **cannot be loaded directly via 
+`transformers`**. Inference requires the LLaVA repository.
+
+**Step 1 — Clone LLaVA:**
+```bash
+git clone https://github.com/haotian-liu/LLaVA.git
+cd LLaVA
+pip install -e .
+```
+
+**Step 2 — Download weights:**
+```python
+from huggingface_hub import snapshot_download
+snapshot_download(
+    repo_id="LabSmart/TEM-LLM", 
+    local_dir="./TEM-LLM"
+)
+```
+
+**Step 3 — Run inference:**
+```bash
+python -m llava.serve.cli \
+    --model-path "./TEM-LLM" \
+    --image-file "path/to/your/tem_image.jpg" \
+    --load-4bit
+```
+
+For the complete pipeline including data crawling, preprocessing, 
+knowledge distillation, training, and evaluation scripts, please 
+refer to the [GitHub repository](https://github.com/SemiMIRTLab/TEM-LLM).
+
+---
+
+## Limitations
+
+- TEM-LLM is optimized for TEM image analysis and may produce 
+  unreliable outputs when applied to out-of-scope questions or 
+  out-of-domain images.
+- The model provides approximate qualitative descriptions rather 
+  than exact quantitative measurements.