Instructions to use Serveurperso/small-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Serveurperso/small-test 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 Serveurperso/small-test:F16
# Run inference directly in the terminal:
llama cli -hf Serveurperso/small-test:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Serveurperso/small-test:F16
# Run inference directly in the terminal:
llama cli -hf Serveurperso/small-test:F16

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 Serveurperso/small-test:F16
# Run inference directly in the terminal:
./llama-cli -hf Serveurperso/small-test:F16

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 Serveurperso/small-test:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Serveurperso/small-test:F16

Use Docker

docker model run hf.co/Serveurperso/small-test:F16

LM Studio
Jan

vLLM

How to use Serveurperso/small-test with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Serveurperso/small-test"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Serveurperso/small-test",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Serveurperso/small-test:F16

Ollama
How to use Serveurperso/small-test with Ollama:
```
ollama run hf.co/Serveurperso/small-test:F16
```
Unsloth Desktop

How to use Serveurperso/small-test with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Serveurperso/small-test:F16

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": "Serveurperso/small-test:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Serveurperso/small-test with Docker Model Runner:
```
docker model run hf.co/Serveurperso/small-test:F16
```

Lemonade

How to use Serveurperso/small-test with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Serveurperso/small-test:F16

Run and chat with the model

lemonade run user.small-test-F16

List all available models

lemonade list

Hermes Agent

How to use Serveurperso/small-test with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Serveurperso/small-test:F16

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 Serveurperso/small-test:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Serveurperso/small-test with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Serveurperso/small-test:F16

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 "Serveurperso/small-test:F16" \
  --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"

small-test / src /run.sh

Serveurperso HF Staff

small-test: a 95M multimodal fixture for the llama.cpp server CI

4a393d1 about 1 month ago

Raw History Blame Contribute Delete

935 Bytes

#!/bin/bash
# usage: run.sh <tag> [train args...]: data prepared once, then text and joint phases on one schedule of STEPS steps (default 17000)
set -e
source env.sh
TAG=$1; shift; STEPS=${STEPS:-17000}
P=${MODEL:-pruned}
MIX="smoltalk:0.47,everyday:0.01,apigen:0.10,hermes_glaive:0.02,hermes_fc:0.005,hermes_fc_single:0.005,nemotron:0.16,synth:0.225,ci_shapes:0.015"
[ -d tokdata ] || { python3 render.py $P; python3 synth_tools.py $P 300000 data/rendered/synth; python3 tokenize_data.py $P tokdata; }
COMMON="--model $P --bs 32 --seq 2048 --mix $MIX --steps $STEPS --warmup 200 --lr-muon 2e-3 --lr-adam 1e-3 --eval-every 500 --gen-every 1000 --save-every 2000"
python3 train.py $COMMON --out ckpt/$TAG-text --name $TAG-text --stop 6000 "$@" > train_$TAG-text.log 2>&1
python3 train.py $COMMON --out ckpt/$TAG-joint --name $TAG-joint --resume ckpt/$TAG-text/resume.pt --ocr-frac 0.35 --ocr-workers 12 "$@" > train_$TAG-joint.log 2>&1

	#!/bin/bash
	# usage: run.sh <tag> [train args...]: data prepared once, then text and joint phases on one schedule of STEPS steps (default 17000)
	set -e
	source env.sh
	TAG=$1; shift; STEPS=${STEPS:-17000}
	P=${MODEL:-pruned}
	MIX="smoltalk:0.47,everyday:0.01,apigen:0.10,hermes_glaive:0.02,hermes_fc:0.005,hermes_fc_single:0.005,nemotron:0.16,synth:0.225,ci_shapes:0.015"
	[ -d tokdata ] \|\| { python3 render.py $P; python3 synth_tools.py $P 300000 data/rendered/synth; python3 tokenize_data.py $P tokdata; }
	COMMON="--model $P --bs 32 --seq 2048 --mix $MIX --steps $STEPS --warmup 200 --lr-muon 2e-3 --lr-adam 1e-3 --eval-every 500 --gen-every 1000 --save-every 2000"
	python3 train.py $COMMON --out ckpt/$TAG-text --name $TAG-text --stop 6000 "$@" > train_$TAG-text.log 2>&1
	python3 train.py $COMMON --out ckpt/$TAG-joint --name $TAG-joint --resume ckpt/$TAG-text/resume.pt --ocr-frac 0.35 --ocr-workers 12 "$@" > train_$TAG-joint.log 2>&1