# 🦙 Fino1-8B – Fine-Tuned Llama 3.1 8B Instruct  

**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.]**.

## 📌 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...]`  
- **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]`  
- **Batch Size**: `[e.g., 16]`  
- **Learning Rate**: `[e.g., 2e-5]`  
- **Epochs**: `[e.g., 3]`  
- **Optimizer**: `[e.g., AdamW, LAMB]`  

## 🔧 Usage  
To use `Fino1-8B` with Hugging Face's `transformers` library:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "your-huggingface-username/Fino1-8B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What are the symptoms of gout?"
inputs = tokenizer(input_text, return_tensors="pt")

output = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(output[0], skip_special_tokens=True))