Instructions to use Orionfold/Saul-7B-Instruct-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Orionfold/Saul-7B-Instruct-v1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Orionfold/Saul-7B-Instruct-v1-GGUF",
	filename="model-F16.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 Orionfold/Saul-7B-Instruct-v1-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/Saul-7B-Instruct-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Orionfold/Saul-7B-Instruct-v1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Orionfold/Saul-7B-Instruct-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Orionfold/Saul-7B-Instruct-v1-GGUF: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 Orionfold/Saul-7B-Instruct-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Orionfold/Saul-7B-Instruct-v1-GGUF: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 Orionfold/Saul-7B-Instruct-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Orionfold/Saul-7B-Instruct-v1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Orionfold/Saul-7B-Instruct-v1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Orionfold/Saul-7B-Instruct-v1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Orionfold/Saul-7B-Instruct-v1-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/Saul-7B-Instruct-v1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Orionfold/Saul-7B-Instruct-v1-GGUF:Q4_K_M

Ollama
How to use Orionfold/Saul-7B-Instruct-v1-GGUF with Ollama:
```
ollama run hf.co/Orionfold/Saul-7B-Instruct-v1-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Orionfold/Saul-7B-Instruct-v1-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/Saul-7B-Instruct-v1-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/Saul-7B-Instruct-v1-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/Saul-7B-Instruct-v1-GGUF to start chatting

Docker Model Runner
How to use Orionfold/Saul-7B-Instruct-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/Orionfold/Saul-7B-Instruct-v1-GGUF:Q4_K_M
```

Lemonade

How to use Orionfold/Saul-7B-Instruct-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Orionfold/Saul-7B-Instruct-v1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Saul-7B-Instruct-v1-GGUF-Q4_K_M

List all available models

lemonade list

Saul 7B Instruct v1 GGUF

GGUF quantizations of Equall/Saul-7B-Instruct-v1, verified end-to-end on the NVIDIA DGX Spark (GB10, 128 GB unified memory).

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 LegalBench (n=50, contains) accuracy. The numbers below are the actual run, not a wishlist.

Variant	Size	Perplexity (wikitext-2)	tok/s on Spark	LegalBench (n=50, contains)
Q4_K_M	4.1 GB	5.986	29.4	62.0%
Q5_K_M	4.8 GB	5.938	20.2	72.0%
Q6_K	5.5 GB	5.925	22.4	68.0%
Q8_0	7.2 GB	5.914	7.3	66.0%
F16	13.5 GB	5.917	10.9	68.0%

Thermal envelope: sustained-load minutes before thermal throttle on a single GB10 = 2 min. Beyond this, expect tok/s degradation; the duty-cycle disclosure is per Orionfold's quant-card standard.

Variants

Variant	Recommended use
Q4_K_M	Best balance — fits comfortably in Spark unified memory at 70B; default pick.
Q5_K_M	Higher quality than Q4_K_M with modest size bump.
Q6_K	Near-lossless; recommended if memory headroom allows.
Q8_0	Effectively lossless; reach for this when quality matters more than throughput.
F16	Reference — no quantization. Use only for measurement / baseline.

How to run

Pull a variant:

huggingface-cli download Orionfold/Saul-7B-Instruct-v1-GGUF model-Q5_K_M.gguf \
  --local-dir ./models/saul-7b-instruct-v1

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

llama-server -m ./models/saul-7b-instruct-v1/model-Q5_K_M.gguf \
  -c 4096 -ngl 99 -t 8 \
  --host 0.0.0.0 --port 8080

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

from llama_cpp import Llama
llm = Llama(
    model_path="./models/saul-7b-instruct-v1/model-Q5_K_M.gguf",
    n_ctx=4096, n_gpu_layers=99, chat_format="mistral",
)
out = llm.create_chat_completion(
    messages=[
        {"role": "user",
         "content": "Does the following sentence overrule a previous case? "
                    "Sentence: 'curtman is overruled to the extent it conflicts with evans.'"}
    ],
    temperature=0.0,
)
print(out["choices"][0]["message"]["content"])

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

Methods

Full methodology and Spark-side measurement protocol: Vertical-curator quants on Spark — Saul-7B-Instruct-v1-GGUF + LegalBench mini-eval.

Other Orionfold vertical curators

Same Spark-tested recipe across the curator-on-Spark series:

finance-chat-GGUF — AdaptLLM finance-chat (Llama-2-7B lineage) for FinanceBench-shaped queries
SecurityLLM-GGUF — Mistral-based cyber-tuned model with CyberMetric mini-eval gating
II-Medical-8B-GGUF — Qwen3-8B + DAPO reasoning for MedMCQA-shaped queries

Each card lists its own measurement quad; the headline numbers are recorded as the actual sweep ran, never pre-corrected.

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

Want to know when the next Orionfold vertical curator drops? Join the launch list at orionfold.com.

Downloads last month: 284

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for Orionfold/Saul-7B-Instruct-v1-GGUF

Base model

Equall/Saul-7B-Instruct-v1

Quantized

(16)

this model