Create README.md

README.md

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+---
+language: 
+  - en
+license: mit
+tags:
+  - finance
+  - custom-model
+  - pytorch
+  - conversational-qa
+  - financial-qa
+datasets:
+  - conv_finqa
+---
+
+# TinyRecursiveModel for ConvFinQA
+
+## Model Details
+
+### Model Description
+
+This model is a custom **TinyRecursiveModel (TRM)** fine-tuned specifically for the **ConvFinQA** dataset. It is designed to handle conversational question answering over complex financial documents, earnings reports, and tables. 
+
+* **Developed by:** KenyaWashed
+* **Model type:** Custom PyTorch Model (`TinyRecursiveModel`)
+* **Language(s) (NLP):** English
+* **License:** MIT
+* **Finetuned from model / Base Tokenizer:** [Điền tên base tokenizer, ví dụ: `roberta-base` hoặc `ProsusAI/finbert`]
+
+## Uses
+
+### Direct Use
+
+The model is built for researchers and developers working in Financial NLP. It can be used to extract answers and perform hierarchical reasoning over financial texts and tables in a conversational context.
+
+### Out-of-Scope Use
+
+This model is not intended to provide professional financial advice or real-time trading signals. It is a research artifact focused on natural language processing and reasoning.
+
+## How to Get Started with the Model
+
+Since this model uses a custom `TinyRecursiveModel` architecture, you will need the original class definition in your codebase to load the weights properly.
+
+Here is how you can load the tokenizer and the model weights:
+
+```python
+import torch
+from transformers import AutoTokenizer
+from huggingface_hub import hf_hub_download
+# Nhớ import class TinyRecursiveModel từ source code của mày
+# from your_custom_module import TinyRecursiveModel 
+
+repo_id = "KenyaWashed/trm-convfinqa"
+
+# 1. Load Tokenizer
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+
+# 2. Khởi tạo model base (nhớ truyền đúng tham số lúc train)
+model = TinyRecursiveModel(
+    # [Điền các tham số khởi tạo model của mày vào đây]
+)
+
+# 3. Download weights từ Hugging Face và load vào model
+model_path = hf_hub_download(repo_id=repo_id, filename="pytorch_model.bin")
+model.load_state_dict(torch.load(model_path, map_location="cpu"))
+model.eval()
+
+print("Model loaded successfully!")