Instructions to use rotextechnologiesuk/nuclear-sqep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rotextechnologiesuk/nuclear-sqep with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rotextechnologiesuk/nuclear-sqep",
	filename="rotex_nuclear_sqep_Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use rotextechnologiesuk/nuclear-sqep with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rotextechnologiesuk/nuclear-sqep:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf rotextechnologiesuk/nuclear-sqep:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rotextechnologiesuk/nuclear-sqep:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf rotextechnologiesuk/nuclear-sqep: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 rotextechnologiesuk/nuclear-sqep:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf rotextechnologiesuk/nuclear-sqep: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 rotextechnologiesuk/nuclear-sqep:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rotextechnologiesuk/nuclear-sqep:Q4_K_M

Use Docker

docker model run hf.co/rotextechnologiesuk/nuclear-sqep:Q4_K_M

LM Studio
Jan

vLLM

How to use rotextechnologiesuk/nuclear-sqep with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/rotextechnologiesuk/nuclear-sqep:Q4_K_M

Ollama
How to use rotextechnologiesuk/nuclear-sqep with Ollama:
```
ollama run hf.co/rotextechnologiesuk/nuclear-sqep:Q4_K_M
```

Unsloth Studio new

How to use rotextechnologiesuk/nuclear-sqep 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 rotextechnologiesuk/nuclear-sqep 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 rotextechnologiesuk/nuclear-sqep to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rotextechnologiesuk/nuclear-sqep to start chatting

Pi new

How to use rotextechnologiesuk/nuclear-sqep with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf rotextechnologiesuk/nuclear-sqep:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "rotextechnologiesuk/nuclear-sqep:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use rotextechnologiesuk/nuclear-sqep with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf rotextechnologiesuk/nuclear-sqep:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default rotextechnologiesuk/nuclear-sqep:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use rotextechnologiesuk/nuclear-sqep with Docker Model Runner:
```
docker model run hf.co/rotextechnologiesuk/nuclear-sqep:Q4_K_M
```

Lemonade

How to use rotextechnologiesuk/nuclear-sqep with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rotextechnologiesuk/nuclear-sqep:Q4_K_M

Run and chat with the model

lemonade run user.nuclear-sqep-Q4_K_M

List all available models

lemonade list

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

⚠️ PUBLIC PREVIEW — NOT OPEN SOURCE. This is a controlled evaluation release. Commercial use, hosted inference, derivative model creation, fine-tuning, distillation, and redistribution are not permitted without a separate written commercial licence from Rotex Technologies Ltd. See license.md.

nuclear-sqep — Public Preview GGUF Model

1. Executive Summary

nuclear-sqep is a public preview GGUF release developed by Rotex Technologies Ltd — a UK-based digital manufacturing, engineering services specialising in CFD, FEA, and technical consultancy across the nuclear, aerospace, automotive, marine, wind, solar, energy storage, and oil & gas sectors.

This release provides a Q4_K_M quantised model intended to demonstrate the capability of our wider commercial model family, whilst preserving our proprietary full‑precision weights, training pipeline, domain‑specific datasets, and deployment optimisations.

✅ This model is suitable for:

Local, offline evaluation and experimentation
Technical benchmarking against publicly reported metrics
Early-stage customer and partner feedback
Investor and technical due diligence
Edge and air-gapped inference testing
Academic and industry technical review

❌ This model is not licensed for:

Commercial deployment or production use
Hosted inference, SaaS, or API services
Fine‑tuning, distillation, or adapter training
Derivative model creation of any kind
Redistribution at scale or mirroring
Use as a component in a competing product or service

For commercial access, evaluation under NDA, or partnership discussions, contact:
📧 sales@rotextechnologies.com

2. Why This Model Exists

Most current models in this parameter class are either:

too large for practical edge and offline deployment;
too generic for specialist industrial and engineering use;
costly to host and difficult to validate in regulated environments;
trained on data of unknown provenance, creating unacceptable IP and compliance risk;
unsuitable for controlled, auditable engineering workflows.

Rotex Technologies Ltd is building a model family focused on:

Domain precision — trained and evaluated on corpora, from 400+ curated technical and engineering literature, focusing in the UK nuclear sector;
Deployability — designed for edge, air-gapped, and resource-constrained environments from the outset;
Auditability — built for workflows where traceability, provenance, and controlled outputs matter;
Data security and privacy — fully air-gapped development pipeline; all processes, from raw document ingestion to model build, are isolated from the internet, ensuring complete confidentiality of datasets and training corpora;
Commercial clarity — a transparent licensing model that respects both open evaluation and proprietary IP.

This public preview is the first release in our production model pipeline — a controlled, quantised snapshot sufficient for meaningful technical evaluation without compromising our proprietary training assets, architecture, or development IP.

3. Target Audience

This preview is intended for:

Audience	Purpose
Technical evaluators	Assess model capability, quality, and behaviour
Engineering teams	Test domain-specific performance on representative tasks
Enterprise innovation groups	Evaluate fit within controlled internal workflows
Investors & due diligence teams	Conduct independent technical assessment
Research partners	Explore collaboration potential
Early design partners	Inform commercial roadmap and integration requirements

It is not intended for, and not licensed for, production deployment of any kind.

4. Model Details

Field	Detail
Model name	rotex_nuclear_sqep
Release type	Public preview (evaluation only)
File format	GGUF
Quantisation	Q4_K_M
Primary language	English
Domain focus	Engineering, technical consultancy, industrial applications
Release date	26 May 2026
Pipeline position	v1.0 — first production release; higher-precision variants (BF16, Q8_0) available to qualified commercial customers
Licence	Model Public Preview Licence v1.0
Commercial use	Not permitted without written licence from Rotex Technologies Ltd
Developer	Rotex Technologies Ltd, United Kingdom

5. What Is Included — and What Is Not

✅ Public in this release:

Q4_K_M GGUF model file
Inference instructions for llama.cpp and compatible runtimes
Evaluation prompts and example outputs
Safety, limitation, and known-issue notes
Public benchmark summary
Full licence terms and acceptable use policy

❌ Not included in this release:

Higher-precision model variants (BF16)
Higher-quality quantisations (Q8_0)
Training, fine‑tuning, or alignment datasets
Fine‑tuning recipes, hyperparameters, or training code
Internal evaluation harness and proprietary benchmarks
Commercial deployment configuration and system prompts
Domain‑specific commercial variants
Any component that would enable derivative model creation

6. Permitted & Prohibited Uses

✅ Permitted (subject to licence terms):

Local, non‑commercial evaluation and experimentation
Technical benchmarking and comparison
Academic and industry review and publication of fair results
Internal proof‑of‑concept assessment
Investor and partner due diligence

❌ Prohibited (without separate written commercial licence):

Production deployment of any kind
Commercial use, revenue generation, or cost reduction
SaaS, API, or hosted inference services
Fine‑tuning, distillation, merging, or adapter creation
Training or improving any other model (competing or otherwise)
Redistribution, mirroring, or repackaging at scale
Any use violating the Acceptable Use Policy

Full terms: license.md

7. Quick Start

Prerequisites

llama.cpp (built with GPU acceleration recommended)
Or any GGUF-compatible runtime (Ollama, LM Studio, etc.)

Basic inference with llama.cpp

# Download the model file from this repository
# Then run:

./llama-cli \
  -m rotex_nuclear_sqep_p2_q4_k_m.gguf \
  -p "Explain the key technical considerations for fatigue analysis in pressure vessel design." \
  -n 512 \
  --temp 0.6 \
  --ctx-size 2048

Interactive mode

./llama-cli \
  -m rotex_nuclear_sqep_p2_q4_k_m.gguf \
  --interactive \
  --temp 0.6 \
  --ctx-size 2048

With Ollama

Create a Modelfile:

FROM ./rotex_nuclear_sqep_p2_q4_k_m.gguf

Then:

ollama create nuclear-sqep-preview -f Modelfile
ollama run nuclear-sqep-preview

Suggested evaluation prompts

"Summarise the key failure modes of a welded joint in cooling water pipes under cyclic thermal loading."

"Outline the regulatory pathway for nuclear safety case submissions under UK ONR guidance."

"Explain the 'Justification' process for new nuclear practices in the UK under the JoPIIRR 2004 regulations. Why does the 2025 Regulatory Review argue that this process currently acts as a technical bottleneck for first-of-a-kind (FOAK) designs?"

8. Evaluation & Benchmarking

We encourage independent benchmarking and technical review. When publishing results:

✅ Attribute the model as: "nuclear-sqep by Rotex Technologies Ltd, used under the Model Public Preview Licence v1.0."
✅ Be transparent about your evaluation methodology, prompt format, and sampling parameters.
✅ Publish both strengths and limitations.
❌ Do not imply endorsement, partnership, or certification by Rotex Technologies Ltd.
❌ Do not disclose any information that would enable reverse engineering or derivative model creation.
❌ Do not use model outputs to train or improve competing models.

Suggested evaluation dimensions

Dimension	Relevance
Domain accuracy	Factual correctness on engineering and technical queries
Instruction following	Adherence to structured technical prompts
Safety & refusal	Appropriate handling of hazardous or regulated queries
Hallucination rate	Propensity to generate plausible but incorrect technical statements
Latency & throughput	Inference speed on representative edge hardware
Quantisation quality	Comparative performance vs. higher-bit quantisations (available commercially)

We welcome feedback on evaluation methodology, benchmark design, and domain coverage at:
📧 info@rotextechnologies.com

9. Known Limitations

As a public preview Q4_K_M quantised model, nuclear-sqep has the following known limitations:

Quantisation degradation — Q4_K_M quantisation intentionally reduces fidelity compared to our full‑precision commercial models. Expect some degradation on nuanced technical reasoning and numerical precision.
No production safety guarantees — This model has not undergone production safety alignment, red‑teaming, or adversarial robustness testing at commercial grade.
Domain coverage — The model's knowledge reflects its training distribution. It may perform less well on topics outside engineering and technical domains.
Hallucination — Like all large language models, nuclear-sqep may generate plausible but factually incorrect output. Never rely on model output for safety‑critical decisions without independent verification.
No regulatory approval — This model has not been assessed or approved by any regulatory body (UK ONR, HSE, EA, NDA, etc.) for use in safety‑critical or regulated applications.
Language — Optimised for English. Performance in other languages is untested and may be unreliable.
Context — The model is focused on UK nuclear sector applications and IAEA-aligned safety principles. Adaptation for other jurisdictions or regulated industries is available on request.

This model is an evaluation artefact. It is not a production‑ready, safety‑assured product.

10. Safety & Responsible Disclosure

We take safety concerns seriously. If you discover:

Generation of hazardous, dangerous, or illegal content
Instructions that could cause physical harm
Bypass of safety guardrails
Biased, discriminatory, or harmful outputs
Vulnerabilities that could be exploited
Copyright or IP concerns in model outputs

Please report responsibly to:
📧 info@rotextechnologies.com

We commit to acknowledging reports within 5 working days and providing a substantive response within 20 working days. We request a 90‑day embargo before public disclosure of vulnerabilities to allow for assessment and mitigation.

Do not use this model to generate, distribute, or facilitate content that would violate applicable laws in the United Kingdom, European Union, United States, People's Republic of China, or any other relevant jurisdiction.

11. Citation

If you reference nuclear-sqep in academic or technical publications:

@software{rotex_nuclear-sqep_2026,
  author = {{Rotex Technologies Ltd}},
  title = {nuclear-sqep — Public Preview GGUF Model},
  year = {2026},
  publisher = {Rotex Technologies Ltd},
  url = {https://huggingface.co/rotextechnologies1/nuclear-sqep},
  note = {Model Public Preview Licence v1.0. Q4_K_M quantisation.}
}

Suggested in-text attribution:
"nuclear-sqep by Rotex Technologies Ltd, used under the Model Public Preview Licence v1.0."

12. Commercial Access

This public preview is not licensed for commercial use. Our commercial offering includes:

Higher-precision (BF16) and higher-quality quantised (Q8_0) model variants
Domain‑specific fine‑tuned variants
Commercial deployment licence
SLAs, support, and integration assistance
Access to proprietary evaluation tooling
Custom development and bespoke model adaptation

For commercial enquiries, partnership discussions, or evaluation under NDA:
📧 sales@rotextechnologies.com
🌐 www.rotextechnologies.com

See COMMERCIAL_USE.md for more detail.

13. Repository Documents

Document	Description
license.md	Full legal terms — Model Public Preview Licence v1.0
ACCEPTABLE_USE_POLICY.md	Acceptable use policy and prohibited use categories
COMMERCIAL_USE.md	Commercial licensing pathway and FAQ

Downloads last month: 32

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

4-bit