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--- |
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pipeline_tag: text-generation |
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base_model: Qwen/Qwen1.5-7B |
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library_name: peft |
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tags: |
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- LoRA |
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- TLE |
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- space-domain-awareness |
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- trajectory-prediction |
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- orbital-mechanics |
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license: other |
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--- |
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# tle-orbit-explainer |
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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. |
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--- |
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## Model Details |
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### Model Description |
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| ------------------ | ----------------------------------------------------------- | |
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| **Developed by** | Jack Al-Kahwati / Stardrive | |
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| **Funded by** | ⬜️ (Self-funded) | |
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| **Shared by** | jackal79 (Hugging Face) | |
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| **Model type** | LoRA adapter (`peft==0.10.0`) | |
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| **Languages** | English | |
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| **License** | TLE-Orbit-NonCommercial v1.0 ([custom terms](./LICENSE.txt)) | |
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| **Finetuned from** | [`Qwen/Qwen1.5-7B`](https://huggingface.co/Qwen/Qwen1.5-7B) | |
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### Model Sources |
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| ---------------- | ---------------------------------------------------------------------------------------------------------- | |
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| **Repository** | [https://huggingface.co/jackal79/tle-orbit-explainer](https://huggingface.co/jackal79/tle-orbit-explainer) | |
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| **Paper / Blog** | https://medium.com/@jack_16944/enhancing-space-awareness-with-fine-tuned-transformer-models-introducing-tle-orbit-explainer-67ae40653ed5 | |
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--- |
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## Uses |
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### Direct Use |
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* Quick summarization of satellite orbital states for analysts |
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* Plain-language TLE explanations for educational purposes |
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* Offline dataset labeling (orbital classifications) |
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### Downstream Use |
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* Combine with SGP4 for enhanced position forecasting |
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* Integration into satellite autonomy stacks (cubesats, small-scale hardware) |
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* Pre-prompted agent support in secure orbital management workflows |
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### Out-of-Scope Use |
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* High-precision orbit propagation without additional physics modeling |
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* Applications related to targeting, weapons systems, or lethal autonomous decisions |
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* Jurisdictions prohibiting ML or data export (verify with ITAR/EAR guidelines) |
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--- |
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## Bias, Risks, & Limitations |
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| Category | Note | |
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| ------------------- | ------------------------------------------------------------------------------------------------------------- | |
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| **Data bias** | Trained primarily on decayed objects (`DECAY = 1`), possibly underestimating longevity for active satellites. | |
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| **Temporal limits** | Operates on snapshot data; does not handle continuous high-frequency time-series. | |
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| **Language** | Supports explanations in English only. | |
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| **Accuracy** | Potential inaccuracies in decay date predictions; verify independently. | |
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### Recommendations |
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Incorporate independent physics-based validation before operational use and maintain a human-in-the-loop for any critical or high-risk decisions. |
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--- |
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## How to Get Started |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline |
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from peft import PeftModel |
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base = "Qwen/Qwen1.5-7B" |
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lora = "jackal79/tle-orbit-explainer" |
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tok = AutoTokenizer.from_pretrained(base) |
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model = AutoModelForCausalLM.from_pretrained(base, device_map="auto") |
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model = PeftModel.from_pretrained(model, lora) # merges LoRA |
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pipe = pipeline("text-generation", model=model, tokenizer=tok, device=0) |
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prompt = """### Prompt: |
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1 25544U 98067A 24079.07757601 .00016717 00000+0 10270-3 0 9994 |
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2 25544 51.6400 337.6640 0007776 35.5310 330.5120 15.50377579499263 |
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### Reasoning: |
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""" |
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print(pipe(prompt, max_new_tokens=120)[0]["generated_text"]) |
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``` |
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--- |
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## License |
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This model is released under the **TLE-Orbit-NonCommercial License v1.0**. |
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- ✅ Free for non-commercial use, research, and internal evaluation |
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- 🚫 Commercial, operational, or for-profit use requires a separate license |
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To request a commercial license, contact: [email protected] |
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