Instructions to use MarshallDoyle/NASA-Systems-Engineering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MarshallDoyle/NASA-Systems-Engineering with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("MarshallDoyle/NASA-Systems-Engineering", dtype="auto")

llama-cpp-python

How to use MarshallDoyle/NASA-Systems-Engineering with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="MarshallDoyle/NASA-Systems-Engineering",
	filename="unsloth.Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use MarshallDoyle/NASA-Systems-Engineering with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MarshallDoyle/NASA-Systems-Engineering:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf MarshallDoyle/NASA-Systems-Engineering:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MarshallDoyle/NASA-Systems-Engineering:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf MarshallDoyle/NASA-Systems-Engineering:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf MarshallDoyle/NASA-Systems-Engineering:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MarshallDoyle/NASA-Systems-Engineering:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf MarshallDoyle/NASA-Systems-Engineering:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MarshallDoyle/NASA-Systems-Engineering:Q4_K_M

Use Docker

docker model run hf.co/MarshallDoyle/NASA-Systems-Engineering:Q4_K_M

LM Studio
Jan
Ollama
How to use MarshallDoyle/NASA-Systems-Engineering with Ollama:
```
ollama run hf.co/MarshallDoyle/NASA-Systems-Engineering:Q4_K_M
```

Unsloth Studio new

How to use MarshallDoyle/NASA-Systems-Engineering with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MarshallDoyle/NASA-Systems-Engineering to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MarshallDoyle/NASA-Systems-Engineering to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MarshallDoyle/NASA-Systems-Engineering to start chatting

Docker Model Runner
How to use MarshallDoyle/NASA-Systems-Engineering with Docker Model Runner:
```
docker model run hf.co/MarshallDoyle/NASA-Systems-Engineering:Q4_K_M
```

Lemonade

How to use MarshallDoyle/NASA-Systems-Engineering with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MarshallDoyle/NASA-Systems-Engineering:Q4_K_M

Run and chat with the model

lemonade run user.NASA-Systems-Engineering-Q4_K_M

List all available models

lemonade list

🚀 NASA Systems Engineering LLaMA-3.1 8B Instruct (Fine-Tuned)

Developed by: MarshallDoyle
Fine-tuned from: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
Specialization: NASA Systems Engineering Handbook (2018) Q&A
Training recipe: LoRA rank=16, epochs=3, steps=285, final loss≈0.40–0.60

✨ Key Features

Feature	Details
Domain Expertise	Deep knowledge of NASA SE lifecycle, terminology, and best practices.
Compact Footprint	+83.8M trainable via LoRA on 8B base.
Long Context	Up to 128K token support for large documents.
Fast Inference	Efficient TRL & Unsloth pipeline.

🎯 Intended Use Cases

Interactive Q&A: Rapid lookup of definitions, processes, and phase purposes.
Summarization: Concise overviews of complex sections in the handbook.
Educational Tools: Training aids for systems engineering students and professionals.
Mission Planning: Quick reference for verification, validation, and lifecycle checkpoints.

📊 Example Comparisons

Phase C Purpose

Model	Response Snippet
Base LLaMA-3.1	“Phase C covers detailed design and preparations for manufacturing…” (generic)
Fine-Tuned	“The main purpose is to complete the detailed design of system elements, fabricate hardware, and code software, ensuring all end products are defined for integration.”

Verification vs. Validation

Model	Response Snippet
Base LLaMA-3.1	“Verification checks requirements; validation checks user needs…” (too brief)
Fine-Tuned	“Verification ensures the system meets specified requirements; validation confirms it satisfies stakeholder expectations in its intended environment.”

⚠️ Limitations

Scope Bounded: Content based on the 2018 handbook; may omit newer updates.
Hallucinations: Possible on out-of-handbook queries.
Bias: NASA-specific terminology; verify critical engineering decisions against official documents.

🔗 Download & Details: Hugging Face model page

Downloads last month: 86

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

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Collection including MarshallDoyle/NASA-Systems-Engineering

NASA Systems Engineering

Collection

This collection contains all the data for performing your own Finetunes of the NASA Systems Engineering Handbook as well as finetuned models. • 7 items • Updated Jun 10, 2025