Instructions to use Orionfold/Advisor-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Orionfold/Advisor-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Orionfold/Advisor-GGUF",
	filename="model-Q8_0.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Orionfold/Advisor-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Orionfold/Advisor-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Orionfold/Advisor-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Orionfold/Advisor-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Orionfold/Advisor-GGUF:Q8_0

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 Orionfold/Advisor-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Orionfold/Advisor-GGUF:Q8_0

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 Orionfold/Advisor-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Orionfold/Advisor-GGUF:Q8_0

Use Docker

docker model run hf.co/Orionfold/Advisor-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use Orionfold/Advisor-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Orionfold/Advisor-GGUF:Q8_0

Ollama
How to use Orionfold/Advisor-GGUF with Ollama:
```
ollama run hf.co/Orionfold/Advisor-GGUF:Q8_0
```

Unsloth Studio

How to use Orionfold/Advisor-GGUF 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 Orionfold/Advisor-GGUF 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 Orionfold/Advisor-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Orionfold/Advisor-GGUF to start chatting

How to use Orionfold/Advisor-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Orionfold/Advisor-GGUF:Q8_0

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": "Orionfold/Advisor-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Orionfold/Advisor-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Orionfold/Advisor-GGUF:Q8_0

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 Orionfold/Advisor-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Orionfold/Advisor-GGUF with Docker Model Runner:
```
docker model run hf.co/Orionfold/Advisor-GGUF:Q8_0
```

Lemonade

How to use Orionfold/Advisor-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Orionfold/Advisor-GGUF:Q8_0

Run and chat with the model

lemonade run user.Advisor-GGUF-Q8_0

List all available models

lemonade list

Orionfold Advisor GGUF

The Orionfold Advisor model lane: NVIDIA-Nemotron-3-Nano-4B fine-tuned for grounded citation discipline, refusal behavior, and workflow routing over a governed retrieval corpus — quantized to Q8_0 GGUF and verified end-to-end on the NVIDIA DGX Spark (GB10, 128 GB unified memory).

What this model does

A governed local AI advisor lane for your enterprise corpus — answers cite exact source ids, refuses when the source isn't there.

Generic local chat models fail the two behaviors an enterprise corpus assistant actually needs: citing the exact source document an answer came from, and refusing cleanly when the corpus does not contain the answer — instead they paraphrase citations, answer from pretraining memory, or fabricate private-looking state under adversarial pretexts. This model is the serving lane of Orionfold Advisor, a governed local advisor appliance: it was fine-tuned on a teacher-verified corpus to hold citation discipline (exact source_id values from the retrieved set, never aliases), a refusal floor that survived novel adversarial pretexts (urgency, roleplay, authority claims, false premises, instructed mis-citation), and Route: workflow handoffs — measured behind a frozen, pre-registered out-of-distribution gate before promotion. On that frozen OOD bench the prompt-engineered 30B baseline it replaced scored 8/21 with 3 fabricated private-state rows; this 4B lane scored 18/21 with refusals 9/9 and zero private-state risk.

Use cases:

grounded Q&A over a retrieval corpus with exact source-id citations
clean refusals on missing-source and private-state questions
workflow routing (Route:) handoffs inside an advisor harness
local-first serving with governed frontier escalation

Who this is for: Operators running a local advisor over a governed corpus on DGX Spark-class hardware (or any llama.cpp host with ~12 GB to spare), and builders evaluating small fine-tuned lanes against prompt-engineered larger baselines.

Spark-tested

Every Orionfold quant ships with a measurement quad on the NVIDIA DGX Spark (GB10, 128 GB unified memory): perplexity, sustained tok/s, thermal envelope, and advisor curveball-v0.2, frozen OOD bench (n=21, scored==strict; refusals 9/9, 0 private-state risk) accuracy. The numbers below are the actual run, not a wishlist.

Variant	Size	Perplexity (wikitext-2)	tok/s on Spark	advisor curveball-v0.2, frozen OOD bench (n=21, scored==strict; refusals 9/9, 0 private-state risk)
Q8_0	4.0 GB	—	42.0	85.7%

Variants

Variant	Recommended use
Q8_0	The promoted Advisor serving lane — effectively lossless, ~12 GB resident with an 8K context on the Spark, warm start ~2 s.

Choosing this lane

Pick this lane to serve Orionfold Advisor behavior locally: it expects retrieval packets (Source N: labelled excerpts plus the Advisor system contract) and answers with Citations: [source_id] lines. Trained with NVIDIA NeMo (LoRA r16 on NVIDIA-Nemotron-3-Nano-4B, merged and exported), quantized with llama.cpp. Run with reasoning off (chat_template_kwargs: {"enable_thinking": false}) to reproduce the measured behavior; the 30B teacher (nemotron-3-nano-30b-a3b) stays a prompt-only comparison lane, not a published artifact.

How to run

Pull a variant:

huggingface-cli download Orionfold/Advisor-GGUF model-Q8_0.gguf \
  --local-dir ./models/advisor

Serve it via llama-server (OpenAI-compatible API):

llama-server -m ./models/advisor/model-Q8_0.gguf \
  -c 8192 -ngl 99 --jinja \
  --host 0.0.0.0 --port 8080

--jinja applies the embedded Nemotron-3 chat template. To reproduce the measured Advisor behavior, keep reasoning off per request:

curl -s http://localhost:8080/v1/chat/completions -H 'Content-Type: application/json' -d '{
  "messages": [{"role": "user", "content": "Question: Which gates must pass before an Orionfold artifact is published?"}],
  "temperature": 0,
  "chat_template_kwargs": {"enable_thinking": false}
}'

Or run in-process via llama-cpp-python:

from llama_cpp import Llama
llm = Llama(
    model_path="./models/advisor/model-Q8_0.gguf",
    n_ctx=4096, n_gpu_layers=99,
)
out = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Question: Which gates must pass before an Orionfold artifact is published?\nAnswer with citations to the supplied sources."}],
    temperature=0.0,
)
print(out["choices"][0]["message"]["content"])

LM Studio and Ollama (via a Modelfile) load the GGUF directly with no additional setup.

Known drift

Bounded limitations observed during Spark-side measurement. Each item below names the artifact and the scope of the drift; the balance of the bench measures clean — see Methods for the full breakdown.

Route: workflow-prefix discipline on "which doc defines X" phrasings without an evaluator hint — 2/5 route rows on curveball-v0.1 rerun; all misses were citation-correct, only the prefix was absent
one over-refusal class out-of-distribution (safe direction) — within the 3/21 misses on frozen curveball-v0.2
the 28/28 frozen held-out shares template machinery with the SFT corpus (in-distribution); treat the frozen OOD curveball as the honest floor — OOD floor 18/21 scored==strict on curveball-v0.2
behavior is contract-shaped: outside Advisor-style packets (system contract + Source N: excerpts) citation/refusal discipline is unmeasured — all published receipts use the packet contract

Other Orionfold variants

Sibling repos from the same release:

Variant	Lane	Format
`Orionfold/Kepler-GGUF`	astrodynamics vertical curator (Qwen3-8B SFT)	gguf

Methods

Full methodology, gate definitions, and the publish decision: Orionfold Advisor — product launch.

Every number above is backed by a tracked receipt in the public monorepo: evidence/orionfold-advisor/ — including the frozen OOD bench (advisor-curveball-v0.2.jsonl, sha12 4b6cac85e41f, frozen before training), the 28-row frozen held-out receipts (28/28 scored==strict on hinted and hint-free packets), the three-lane curveball comparison (advisor-curveball2-compare-v0.1.json), and the §14 publish receipt (advisor-publish-receipt-v0.1.json, verdict PROMOTED, 9/9 gates).

Published by Orionfold LLC · orionfold.com · Methods documented at ainative.business/field-notes.