Instructions to use esa-sceva/llama3-satcom-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use esa-sceva/llama3-satcom-8b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="esa-sceva/llama3-satcom-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("esa-sceva/llama3-satcom-8b")
model = AutoModelForCausalLM.from_pretrained("esa-sceva/llama3-satcom-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use esa-sceva/llama3-satcom-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "esa-sceva/llama3-satcom-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "esa-sceva/llama3-satcom-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/esa-sceva/llama3-satcom-8b

SGLang

How to use esa-sceva/llama3-satcom-8b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "esa-sceva/llama3-satcom-8b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "esa-sceva/llama3-satcom-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "esa-sceva/llama3-satcom-8b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "esa-sceva/llama3-satcom-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use esa-sceva/llama3-satcom-8b with Docker Model Runner:
```
docker model run hf.co/esa-sceva/llama3-satcom-8b
```

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

Model	MCQA (Accuracy)			Satcom-QA			EVE-QA
Model	Satcom	EVE	TeleQnA	Norm.	Bin.	WR	Norm.	Bin.	WR
Llama-3.1-8B-Instruct	78.59	81.35	68.40	75.44	70.40	—	61.47	65.20	—
`llama3-satcom-8b`	80.15	80.95	68.80	77.75	73.62	51.49	62.90	68.12	51.41

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.

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for esa-sceva/llama3-satcom-8b

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct