README.md

---
license: mit
datasets:
- Taylor658/titan-hohmann-transfer-orbit
language:
- en
base_model:
- mistralai/Pixtral-12B-Base-2409
tags:
- mistral
- pixtral
- vlm
- multimodal
- image-text-to-text
- orbital-mechanics
- hohmann-transfer-orbits
library_name: transformers
pipeline_tag: image-text-to-text
model_type: pixtral
---

# 🚀 Pixtral 12B Fine-Tuned on Titan-Hohmann-Transfer-Orbit

> ✨ **Updated** to`mistralai/Pixtral-12B-Base-2409`.

## 🌟 Overview

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

- **Base**: `mistralai/Pixtral-12B-Base-2409`
- **Type**: 🖼️ Multimodal (Vision + Text)
- **Params**: ~12B (decoder) + vision encoder
- **Languages**: 🇺🇸 English
- **License**: 📄 MIT

## 🎯 Intended Use

- 🛰️ Hohmann transfer ∆v estimation
- ⏱️ Transfer-time approximations
- 🔍 Orbit analysis aids and reasoning

## 🚀 Quickstart

### 🌐 vLLM (multimodal)
```python
from vllm import LLM
from vllm.sampling_params import SamplingParams

llm = LLM(model="mistralai/Pixtral-12B-Base-2409", tokenizer_mode="mistral")
sampling = SamplingParams(max_tokens=512, temperature=0.2)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Given this diagram, estimate the delta-v for a Hohmann transfer to Titan."},
            {"type": "image_url", "image_url": {"url": "https://example.com/orbit_diagram.png"}}
        ]
    }
]
resp = llm.chat(messages, sampling_params=sampling)
print(resp[0].outputs[0].text)
```

### 🤗 Transformers (text-only demo)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "mistralai/Pixtral-12B-Base-2409"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

prompt = "Compute approximate delta-v for a Hohmann transfer to Titan. State assumptions."
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=512, temperature=0.2)
print(tok.decode(out[0], skip_special_tokens=True))
```

## 📊 Training Data

- **Dataset**: `Taylor658/titan-hohmann-transfer-orbit`
- **Modalities**: 📝 text (explanations), 💻 code (snippets), 🖼️ images (orbital diagrams)

## ⚠️ Limitations

- 🎯 Optimized for Hohmann transfers and related reasoning
- 💾 Requires sufficient GPU VRAM for best throughput

## 🙏 Acknowledgements

- **Base model** by Mistral AI (Pixtral 12B)
- **Dataset** by A Taylor

### 📞 Contact Information

- **Author**: 👨‍🚀 A Taylor
- **Email**: 📧 
- **Repository**: 🔗 https://github.com/ATaylorAerospace/HohmannHET

---