Update README.md

README.md

@@ -14,42 +14,36 @@ tags:
 - image-text-to-text
 - orbital-mechanics
 - hohmann-transfer-orbits
----
-language:
-- en
-license: mit
 library_name: transformers
 pipeline_tag: image-text-to-text
-base_model: mistralai/Pixtral-12B-Base-2409
-datasets:
-- Taylor658/titan-hohmann-transfer-orbit
+model_type: pixtral
 ---
 
-# Pixtral 12B Fine-Tuned on Titan-Hohmann-Transfer-Orbit
+# 🚀 Pixtral 12B Fine-Tuned on Titan-Hohmann-Transfer-Orbit
 
-> Updated to the latest suitable Mistral multimodal base: `mistralai/Pixtral-12B-Base-2409`.
+> ✨ **Updated** to the latest suitable Mistral multimodal base: `mistralai/Pixtral-12B-Base-2409`.
 
-## Overview
+## 🌟 Overview
 
-Fine-tuned variant of Pixtral 12B for orbital mechanics with emphasis on Hohmann transfer orbits. Supports multimodal (image + text) inputs and text outputs.
+Fine-tuned variant of **Pixtral 12B** for **orbital mechanics** with emphasis on **Hohmann transfer orbits**. Supports multimodal (image + text) inputs and text outputs.
 
-## Model Details
+## 🔧 Model Details
 
-- Base: `mistralai/Pixtral-12B-Base-2409`
-- Type: Multimodal (Vision + Text)
-- Params: ~12B (decoder) + vision encoder
-- Languages: English
-- License: MIT
+- **Base**: `mistralai/Pixtral-12B-Base-2409`
+- **Type**: 🖼️ Multimodal (Vision + Text)
+- **Params**: ~12B (decoder) + vision encoder
+- **Languages**: 🇺🇸 English
+- **License**: 📄 MIT
 
-## Intended Use
+## 🎯 Intended Use
 
-- Hohmann transfer ∆v estimation
-- Transfer-time approximations
-- Orbit analysis aids and reasoning
+- 🛰️ Hohmann transfer ∆v estimation
+- ⏱️ Transfer-time approximations
+- 🔍 Orbit analysis aids and reasoning
 
-## Quickstart
+## 🚀 Quickstart
 
-### vLLM (multimodal)
+### 🌐 vLLM (multimodal)
 ```python
 from vllm import LLM
 from vllm.sampling_params import SamplingParams
@@ -70,7 +64,7 @@ resp = llm.chat(messages, sampling_params=sampling)
 print(resp[0].outputs[0].text)
 ```
 
-### Transformers (text-only demo)
+### 🤗 Transformers (text-only demo)
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
@@ -85,25 +79,25 @@ out = model.generate(**inputs, max_new_tokens=512, temperature=0.2)
 print(tok.decode(out[0], skip_special_tokens=True))
 ```
 
-## Training Data
+## 📊 Training Data
 
-- Dataset: `Taylor658/titan-hohmann-transfer-orbit`
-- Modalities: text (explanations), code (snippets), images (orbital diagrams)
+- **Dataset**: `Taylor658/titan-hohmann-transfer-orbit`
+- **Modalities**: 📝 text (explanations), 💻 code (snippets), 🖼️ images (orbital diagrams)
 
-## Limitations
+## ⚠️ Limitations
 
-- Optimized for Hohmann transfers and related reasoning
-- Requires sufficient GPU VRAM for best throughput
+- 🎯 Optimized for Hohmann transfers and related reasoning
+- 💾 Requires sufficient GPU VRAM for best throughput
 
-## Acknowledgements
+## 🙏 Acknowledgements
 
-- Base model by Mistral AI (Pixtral 12B)
-- Dataset by A Taylor
+- **Base model** by Mistral AI (Pixtral 12B)
+- **Dataset** by A Taylor
 
-### Contact Information
+### 📞 Contact Information
 
-- **Author**: A Taylor
-- **Email** 
-- **Repository**: https://github.com/ATaylorAerospace/HohmannHET
+- **Author**: 👨‍🚀 A Taylor
+- **Email**: 📧 
+- **Repository**: 🔗 https://github.com/ATaylorAerospace/HohmannHET
 
 ---