Instructions to use StandardThinking/StandardOne-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use StandardThinking/StandardOne-8B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf StandardThinking/StandardOne-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf StandardThinking/StandardOne-8B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf StandardThinking/StandardOne-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf StandardThinking/StandardOne-8B-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 StandardThinking/StandardOne-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf StandardThinking/StandardOne-8B-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 StandardThinking/StandardOne-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf StandardThinking/StandardOne-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/StandardThinking/StandardOne-8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use StandardThinking/StandardOne-8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "StandardThinking/StandardOne-8B-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": "StandardThinking/StandardOne-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/StandardThinking/StandardOne-8B-GGUF:Q4_K_M

Ollama
How to use StandardThinking/StandardOne-8B-GGUF with Ollama:
```
ollama run hf.co/StandardThinking/StandardOne-8B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use StandardThinking/StandardOne-8B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf StandardThinking/StandardOne-8B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "StandardThinking/StandardOne-8B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use StandardThinking/StandardOne-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/StandardThinking/StandardOne-8B-GGUF:Q4_K_M
```

Lemonade

How to use StandardThinking/StandardOne-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull StandardThinking/StandardOne-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.StandardOne-8B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use StandardThinking/StandardOne-8B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf StandardThinking/StandardOne-8B-GGUF: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 StandardThinking/StandardOne-8B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use StandardThinking/StandardOne-8B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf StandardThinking/StandardOne-8B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "StandardThinking/StandardOne-8B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Standard One 8B — GGUF

Updated weights (v2, 2026-09-26). These files are built from Standard One 8B v2. If you downloaded them before, download them again or pin revision="v2". Earlier versions stay available under the tags v1 and v1.1.

Version: v2

GGUF builds of Standard One 8B (Ministral 3 8B text + Pixtral vision tower, mistral3 architecture) for use with llama.cpp.

Files

Most quant levels below (marked "imatrix") were built with an importance matrix calibrated on 1,512 prompts sampled from our own training rows (see "Importance-matrix calibration" below), which recovers some of the accuracy quantization would otherwise lose. Q8_0 and BF16 don't need one.

File	Quant	Size	imatrix
`StandardOne-8B-BF16.gguf`	BF16 (no quantization)	17.0 GB	—
`StandardOne-8B-Q8_0.gguf`	Q8_0	9.0 GB	no
`StandardOne-8B-Q5_K_M.gguf`	Q5_K_M	6.1 GB	yes
`StandardOne-8B-Q4_K_M.gguf`	Q4_K_M	5.2 GB	yes
`StandardOne-8B-IQ4_XS.gguf`	IQ4_XS	4.7 GB	yes
`StandardOne-8B-Q3_K_M.gguf`	Q3_K_M	4.2 GB	yes
`StandardOne-8B-IQ3_M.gguf`	IQ3_M	4.0 GB	yes
`StandardOne-8B-Q2_K.gguf`	Q2_K	3.4 GB	yes
`StandardOne-8B-IQ2_M.gguf`	IQ2_M	3.1 GB	yes
`mmproj-StandardOne-8B.gguf`	F16 vision projector	857 MB	—

SHA256 checksums: SHA256SUMS. Source revisions, conversion tool version and full validation numbers: release-manifest.json.

Q4_K_M note: this is the imatrix-calibrated version, not a plain quantization. We generated both and chose whichever scored higher on the mean of 10 held-out and public-dataset decision suites (no JevBench items; imatrix 76.56 vs. plain 76.33).

Usage

Text-only:

llama-cli -m StandardOne-8B-Q4_K_M.gguf -ngl 99 -p "Your prompt"

With vision (image input):

llama-server -m StandardOne-8B-Q4_K_M.gguf --mmproj mmproj-StandardOne-8B.gguf -ngl 99

The GGUF's own embedded chat template (converted from the model's chat_template.jinja) is applied automatically; no extra flags needed for chat formatting.

Importance-matrix calibration

Q5_K_M down to IQ2_M were quantized with llama-imatrix calibrated on 1,512 prompts (42 per cohort across 36 training-data cohorts; training data only — no benchmark/held-out file was used), context 2048. Q8_0 and BF16 don't use an imatrix (high enough precision that it doesn't move the needle).

Validation

Accuracy was checked by comparing next-token logits over the option letters on the JevBench public suites (easy/original/hard) against the served BF16 baseline; see release-manifest.json for full methodology and gguf-validation.md (in the release kit) for the complete writeup. Measured (accuracy %, n=48/72/111 for easy/original/hard; prefill tokens/sec is the mean over the 231 scored decisions):

Quant	Easy	Original	Hard	Overall	Prefill tok/s
served BF16 (reference)	100.0	93.06	54.95	—	—
BF16-GGUF	97.92	97.22	54.05	76.62	3,771
Q8_0	97.92	97.22	54.05	76.62	2,590
Q5_K_M	97.92	97.22	56.76	77.92	1,914
Q4_K_M (shipped, imatrix)	97.92	97.22	54.95	77.06	1,984
IQ4_XS	97.92	97.22	57.66	78.35	2,446
Q3_K_M	97.92	98.61	53.15	76.62	1,824
IQ3_M	100.0	97.22	56.76	78.35	2,185
Q2_K	100.0	95.83	54.95	77.06	1,732
IQ2_M	100.0	90.28	54.05	74.89	1,927

Note on the mmproj conversion

llama.cpp's stock --mmproj converter (as of the commit used here) drops the [IMG_BREAK] token embedding for HF-format Mistral3ForConditionalGeneration checkpoints (a filter meant to strip text-model tensors also strips the one row of the text embedding matrix the vision projector needs), so the mmproj file it produces fails to load in llama-server/llama-cli ("unable to find tensor v.token_embd.img_break"). The mmproj file in this folder was built with a small local patch that lets that one tensor through; see release-manifest.json -> known_issues_fixed for details. It loads and runs correctly with --mmproj.

License

Apache License 2.0 — see LICENSE and NOTICE. Same terms as the source StandardOne-8B release; this GGUF conversion adds no additional restrictions.

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GGUF

Model size

8B params

Architecture

mistral3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

8-bit

16-bit

Model tree for StandardThinking/StandardOne-8B-GGUF

Base model

mistralai/Ministral-3-8B-Base-2512

Finetuned

mistralai/Ministral-3-8B-Instruct-2512-BF16

Finetuned

StandardThinking/StandardOne-8B

Quantized

(2)

this model