Update README.md

README.md

@@ -1,22 +1,18 @@
-# 🦙 Fino1-8B – Fine-Tuned Llama 3.1 8B Instruct  
+# 🦙 Fino1-8B
 
-**Fino1-8B** is a fine-tuned version of **Llama 3.1 8B Instruct**, designed to improve performance on **[specific task/domain]**. This model has been trained using **supervised fine-tuning (SFT)** on **[dataset name]**, enhancing its capabilities in **[use cases such as medical Q&A, legal text summarization, SQL generation, etc.]**.
+**Fino1-8B** is a fine-tuned version of **Llama 3.1 8B Instruct**, designed to improve performance on **[financial reasoning tasks]**. This model has been trained using **SFT** and **RF** on **TheFinAI/Fino1_Reasoning_Path_FinQA**, enhancing its capabilities in **financial reasoning tasks**.
 
 ## 📌 Model Details  
 - **Model Name**: `Fino1-8B`  
 - **Base Model**: `Meta Llama 3.1 8B Instruct`  
-- **Fine-Tuned On**: `[Dataset Name(s)]`  
-- **Training Method**: Supervised Fine-Tuning (SFT) *(mention if RLHF or other techniques were used)*  
-- **Objective**: `[Enhance performance on specific tasks such as...]`  
+- **Fine-Tuned On**: `TheFinAI/Fino1_Reasoning_Path_FinQA` Derived from FinQA dataset.  
+- **Training Method**: SFT and RF  
+- **Objective**: `[Enhance performance on specific tasks such as financial mathemtical reasoning]`  
 - **Tokenizer**: Inherited from `Llama 3.1 8B Instruct`  
 
-## 🚀 Capabilities  
-- ✅ **[Capability 1]** (e.g., improved response accuracy for medical questions)  
-- ✅ **[Capability 2]** (e.g., better SQL query generation for structured databases)  
-- ✅ **[Capability 3]** (e.g., more context-aware completions for long-form text)  
 
 ## 📊 Training Configuration  
-- **Training Hardware**: `GPU: [e.g., 8x A100, H100]`  
+- **Training Hardware**: `GPU: [e.g., 4xH100]`  
 - **Batch Size**: `[e.g., 16]`  
 - **Learning Rate**: `[e.g., 2e-5]`  
 - **Epochs**: `[e.g., 3]`  
@@ -28,12 +24,12 @@ To use `Fino1-8B` with Hugging Face's `transformers` library:
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model_name = "your-huggingface-username/Fino1-8B"
+model_name = "TheFinAI/Fino1-8B"
 
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
-input_text = "What are the symptoms of gout?"
+input_text = "What is the results of 3-5?"
 inputs = tokenizer(input_text, return_tensors="pt")
 
 output = model.generate(**inputs, max_new_tokens=200)