README.md

---
base_model: Qwen/Qwen1.5-7B
library_name: peft
tags:
  - LoRA
  - TLE
  - space-domain-awareness
  - trajectory-prediction
  - orbital-mechanics
license: other        
---

# tle-orbit-explainer

A LoRA adapter for **Qwen-1.5-7B** that translates raw Two-Line Elements (TLEs) into natural-language orbit explanations, decay risk scores, and anomaly flags for general space awareness workflows.

---

## Model Details

### Model Description

|                    |                                                             |
| ------------------ | ----------------------------------------------------------- |
| **Developed by**   | Jack Al-Kahwati / Stardrive                                 |
| **Funded by**      | ⬜️ (Self-funded)                                            |
| **Shared by**      | jackal79 (Hugging Face)                                     |
| **Model type**     | LoRA adapter (`peft==0.10.0`)                               |
| **Languages**      | English                                                     |
| **License**        | apache-2.0                                                  |
| **Finetuned from** | [`Qwen/Qwen1.5-7B`](https://huggingface.co/Qwen/Qwen1.5-7B) |

### Model Sources

|                  |                                                                                                            |
| ---------------- | ---------------------------------------------------------------------------------------------------------- |
| **Repository**   | [https://huggingface.co/jackal79/tle-orbit-explainer](https://huggingface.co/jackal79/tle-orbit-explainer) |
| **Paper / Blog** | https://medium.com/@jack_16944/enhancing-space-awareness-with-fine-tuned-transformer-models-introducing-tle-orbit-explainer-67ae40653ed5                                                                                        |
---

## Uses

### Direct Use

* Quick summarization of satellite orbital states for analysts
* Plain-language TLE explanations for educational purposes
* Offline dataset labeling (orbital classifications)

### Downstream Use

* Combine with SGP4 for enhanced position forecasting
* Integration into satellite autonomy stacks (cubesats, small-scale hardware)
* Pre-prompted agent support in secure orbital management workflows

### Out-of-Scope Use

* High-precision orbit propagation without additional physics modeling
* Applications related to targeting, weapons systems, or lethal autonomous decisions
* Jurisdictions prohibiting ML or data export (verify with ITAR/EAR guidelines)

---

## Bias, Risks, & Limitations

| Category            | Note                                                                                                          |
| ------------------- | ------------------------------------------------------------------------------------------------------------- |
| **Data bias**       | Trained primarily on decayed objects (`DECAY = 1`), possibly underestimating longevity for active satellites. |
| **Temporal limits** | Operates on snapshot data; does not handle continuous high-frequency time-series.                             |
| **Language**        | Supports explanations in English only.                                                                        |
| **Accuracy**        | Potential inaccuracies in decay date predictions; verify independently.                                       |

### Recommendations

Incorporate independent physics-based validation before operational use and maintain a human-in-the-loop for any critical or high-risk decisions.

---

## How to Get Started

```python
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from peft import PeftModel

base = "Qwen/Qwen1.5-7B"
lora = "jackal79/tle-orbit-explainer"

tok   = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
model = PeftModel.from_pretrained(model, lora)  # merges LoRA

pipe = pipeline("text-generation", model=model, tokenizer=tok, device=0)

prompt = """### Prompt:
1 25544U 98067A   24079.07757601 .00016717 00000+0 10270-3 0  9994
2 25544  51.6400 337.6640 0007776  35.5310 330.5120 15.50377579499263

### Reasoning:
"""
print(pipe(prompt, max_new_tokens=120)[0]["generated_text"])
```