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Model: esa-sceva/llama3-satcom-8b
Source: Original Platform

README.md

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+---
+license: llama3.1
+base_model:
+- meta-llama/Llama-3.1-8B-Instruct
+tags:
+- satcom
+- satellite
+- satellite communications
+- space
+- esa
+- artes
+- llm
+- fine-tuned
+- question-answering
+language:
+- en
+model_name: esa-sceva/llama3-satcom-8b
+pipeline_tag: text-generation
+library_name: transformers
+datasets:
+- esa-sceva/satcom-qa
+- esa-sceva/satcom-mcqa
+- esa-sceva/satcom-synth-qa
+- esa-sceva/satcom-synth-qa-cot
+---
+
+# LLama3 SatCom 8B
+
+**LLama3 SatCom 8B** is a fine-tuned open Large Language Model (LLM) developed under the **ESA ARTES** programme as part of the **SatcomLLM / SCEVA (SatCom Expert Virtual Assistant)** project.  
+It is designed to support **satellite communications (SatCom)** experts, engineers, and mission planners through domain-specialised reasoning, question answering, and document-based assistance.
+
+---
+
+## Model Description
+
+- **Base model:** `meta-llama/Llama-3.1-8B-Instruct`  
+- **Fine-tuning type:** Instruction fine-tuning (IFT)  
+- **Training data:** Domain-specific question–answer datasets (manual, synthetic, and multiple-choice)  
+- **Architecture:** Decoder-only transformer, 8 billion parameters  
+- **Languages:** English  
+- **License:** LLama-3.1 Communiy License Agreement  
+
+The model has been fine-tuned on curated SatCom-related corpora to enhance its understanding of technical language, protocols, and reasoning processes common to satellite communications, including 5G/6G non-terrestrial networks, link budget evaluation, and mission engineering tasks.
+
+---
+
+## Training Datasets
+
+| Dataset                           | Description                                                                            |
+| --------------------------------- | -------------------------------------------------------------------------------------- |
+| **esa-sceva/satcom-synth-qa**     | Synthetic QA data generated via agentic pipelines using large teacher models           |
+| **esa-sceva/satcom-synth-qa-cot** | Chain-of-thought annotated QA used to improve reasoning depth and factual traceability |
+
+---
+
+
+## Intended Use
+
+**Primary use cases:**
+- Technical Q&A and reasoning on SatCom systems  
+- Support for link budget and RF engineering questions  
+- Guidance for 5G/6G NTN (Non-Terrestrial Network) operations  
+- Mission design, planning, and anomaly detection support  
+- Educational and research use within the SatCom sector  
+
+**Intended users:**  
+- ESA engineers and project officers  
+- SatCom and aerospace researchers  
+- SMEs and technical operators in satellite communication  
+- Academic and educational users  
+
+---
+
+## Limitations
+
+- The model does **not access real-time mission data** or proprietary ESA documents.  
+- Answers are based on training data and may require expert validation for operational use.  
+- It should not be relied upon for flight-critical or safety-critical decisions.  
+- Limited context window (base 8B configuration) may constrain long-document reasoning.
+
+---
+
+## Technical Details
+
+| Parameter | Value |
+|------------|--------|
+| Base Model | Llama 3.1 8B Instruct |
+| Parameters | 8 billion |
+| Context length | 8k tokens |
+| Precision | bfloat16 / fp16 |
+| Framework | Lit-GPT (Lightning AI) |
+| Training infra | EuroHPC MareNostrum5 + AWS EC2 |
+| Optimisation | LoRA fine-tuning, cosine LR schedule |
+
+---
+
+## Evaluation
+
+### Evaluation Datasets
+
+The model was benchmarked on both general-purpose and domain-specific QA tasks. Regarding Satcom-specific datasets:
+
+| Dataset                   | Subset             | Description                                                                                          |
+| ------------------------- | ------------------ | ---------------------------------------------------------------------------------------------------- |
+| **esa-sceva/satcom-qa**   | **Open SatCom QA** | Conceptual and reasoning-based questions on SatCom workflows, regulations, and mission/system design |
+|                           | **Math SatCom QA** | Quantitative and formula-based questions derived from system design and orbital mechanics topics     |
+| **esa-sceva/satcom-mcqa** | **Open MCQA**      | Conceptual multiple-choice questions on RF systems, communication protocols, and architecture        |
+|                           | **Math MCQA**      | Numerical and link-budget-focused multiple-choice questions testing applied calculations             |
+
+### Results
+
+<table>
+  <thead>
+    <tr>
+      <th rowspan="2">Model</th>
+      <th colspan="3">MCQA (Accuracy)</th>
+      <th colspan="3">Satcom-QA</th>
+      <th colspan="3">EVE-QA</th>
+    </tr>
+    <tr>
+      <th>Satcom</th>
+      <th>EVE</th>
+      <th>TeleQnA</th>
+      <th>Norm.</th>
+      <th>Bin.</th>
+      <th>WR</th>
+      <th>Norm.</th>
+      <th>Bin.</th>
+      <th>WR</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>Llama-3.1-8B-Instruct</td>
+      <td>78.59</td>
+      <td><strong>81.35</strong></td>
+      <td>68.40</td>
+      <td>75.44</td>
+      <td>70.40</td>
+      <td>—</td>
+      <td>61.47</td>
+      <td>65.20</td>
+      <td>—</td>
+    </tr>
+    <tr style="border-top: 1px dashed #999;">
+      <td><code>llama3-satcom-8b</code></td>
+      <td><strong>80.15</strong></td>
+      <td>80.95</td>
+      <td><strong>68.80</strong></td>
+      <td><strong>77.75</strong></td>
+      <td><strong>73.62</strong></td>
+      <td><strong>51.49</strong></td>
+      <td><strong>62.90</strong></td>
+      <td><strong>68.12</strong></td>
+      <td><strong>51.41</strong></td>
+    </tr>
+  </tbody>
+</table>
+
+**Table:** Evaluation results (%).  
+ - **Norm.** denotes the normalized score obtained by averaging 1–5 ratings from a panel of LLM judges (Qwen3, gpt-4.1-mini, Mistral-Large-2512, and DeepSeek-V3.2) and scaling to [0,1].  
+ - **Bin.** is the binary accuracy computed from correctness judgments.  
+ - **WR** (Adjusted Win Rate) is defined as (wins + 0.5 × ties) / total, based on pairwise comparisons with randomized answer order.  
+Multiple-choice performance is measured using standard accuracy.  
+All results are averaged over 3–5 runs; standard deviation ≤ 0.25 pp for open-ended QA and ≤ 0.10 pp for MCQA.
+
+---