---
base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- gpt_oss
- trl
- nasa
- standards
license: apache-2.0
language:
- en
---

# NASA OSS Model Card

## Highlights

- Fine-tune of **OpenAI GPT-OSS** (20B) using [Unsloth](https://github.com/unslothai/unsloth) for optimized training.  
- Trained on **synthetic Q&A data** derived from all available NASA standards and handbooks (excluding center-level standards).  
- Data generated via **chunking into 4096 tokens with 256 overlap**, question + answer pairs produced per chunk.  
- Provides **compliance-oriented, clause-referenced outputs** for NASA engineering standards.  
- Extends prior work ([NASA Systems Engineering Llama-3.1 8B](https://huggingface.co/MarshallDoyle/NASA-Systems-Engineering)) to **dozens of NASA standards**, not just one handbook.  
- **Best used with retrieval-augmented generation (RAG)**: include the relevant standard text in the prompt for highest accuracy.  

**Recommended Inference Settings:**  
- `temperature = 1`  
- `top_k = 0`  
- `top_p = 1`  

**Official OpenAI GPT-OSS page:** [https://openai.com/oss](https://openai.com/oss)

---

## Model Summary

The **NASA OSS model** is designed to assist with **interpretation, reasoning, and compliance guidance across NASA standards and handbooks**.  
It is particularly effective for:  

- Engineering decision support  
- Standards compliance verification  
- Requirements and process traceability  
- Education and training of new engineers  

---

## Training Data

- **Sources:** All publicly available NASA standards and handbooks (excluding center-level standards).  
- **Processing pipeline:**  
  - Documents chunked into 4096 tokens with 256-token overlap.  
  - Each chunk used to generate multiple **synthetic questions** relevant only to that section.  
  - **Answers generated** strictly from the chunk (but phrased naturally without explicit chunk references).  

---

## Recommended Usage

- **Use with RAG:** Retrieval of the intended NASA standard or handbook section and injection into the prompt significantly improves performance.  
- **Verification:** Always cross-check outputs against the official NASA source documents before applying in mission-critical work.  

---

## Comparison Examples

### Example 1 – Required Components for Material Process Qualification (NASA-STD-6030)

**Llama-3.1 8B Fine-tune (Systems Eng only):**  
"Nasa standards generally require process control and material specifications before flight qualification."  

**GPT-OSS Base:**  
"Material specifications and process definitions must be established for qualification."  

**NASA OSS:**  
NASA-STD-6030 – Materials Process Qualification  
The standard requires that a candidate material process be fully characterized before it can be qualified for use in additive manufacturing hardware. Three distinct components must be defined and documented:  
1. Feedstock Material Specification – properties of the raw AM material.  
2. Part Material Specification (or equivalent) – final part-material characteristics after processing.  
3. Material Processing Specification (MPS) – process parameters and QA procedures.  
These items are listed in NASA-STD-6030 §2 f (g–i) and clarified in §8 as required for a Qualified Part Process.  

---

### Example 2 – PCRDs (Primary Control-Relevant Data) for Metallic AM Processes

**Llama-3.1 8B Fine-tune:**  
"Typical values include tensile strength and elongation."  

**GPT-OSS Base:**  
"PCRDs are mechanical properties but fracture toughness is sometimes excluded."  

**NASA OSS:**  
Question: Which of the following is NOT one of the four PCRDs defined for each metallic AM MPS: ultimate tensile strength, yield strength, elongation, or fracture toughness?  
Answer: Fracture toughness is NOT one of the PCRDs.  
NASA-STD-6030 explicitly defines: ultimate tensile strength, yield strength, elongation, and reduction of area.  
Reference: NASA-STD-6030 §4.3.2  

---

### Example 3 – Powder Certification and Reuse Rules for Ti-6Al-4V (LPBF)

**Llama-3.1 8B Fine-tune:**  
"Powder must be certified before use in additive manufacturing."  

**GPT-OSS Base:**  
"Certification and reuse requirements depend on program standards."  

**NASA OSS:**  
NASA-STD-6030 requires:  
- Feedstock certification for composition, cleanliness, and particle morphology (§5.1).  
- Documentation of lot traceability and maximum reuse cycles (§5.2).  
- Validation via witness coupon testing before production (§7.3).  
These steps ensure powder consistency and qualification evidence for flight hardware.  

---

### Example 4 – Wire-Feed Directed Energy Deposition (DED) Qualification

**NASA OSS:**  
For wire-feed DED processes, NASA-STD-6030 mandates documentation of:  
- Material Specification – composition, purity, heat-treatment requirements (§7.2).  
- Processing Parameters – machine-specific build settings and post-processing (§7.3).  
- Testing & Inspection Methods – destructive and nondestructive evaluations with acceptance criteria (§7.3).  
These form the Candidate Material Process (CMP) and serve as the foundation for establishing a Qualified Part Process (QPP).  

---

### Example 5 – Dimensional Inspection for AM Structural Truss

**NASA OSS:**  
NASA-STD-6030 requires GD&T compliance verification and interface checks through the Additive Manufacturing Control Plan (AMCP).  
- MPS, QMP, and AMCP integration define dimensional verification (§4.1–4.3).  
- Witness coupon testing and statistical sampling ensure dimensional repeatability (§7.2–7.3).  
Reference: NASA-STD-6030, §4.2; §7.2–7.3  

---

## Limitations

- Model outputs reflect **public NASA standards only**.  
- May not cover internal center-level or proprietary standards.  
- **Best used with retrieval context** – performance drops without standard text injection.  

---

## Ethical Considerations

- Should be treated as an **assistive tool**, not as a replacement for human engineering judgment.  
- Outputs must be verified against authoritative NASA documentation.  
- Not suitable for export-controlled, ITAR-restricted, or classified projects.  

---

## Citation

If you use this model, please cite as:

@misc{marshall2025nasaoss,
  author = {Marshall Doyle},
  title = {NASA OSS: Domain-Specific Fine-Tune of GPT OSS on NASA Standards},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/MarshallDoyle/NASA-OSS}}
}

---