Instructions to use valentin0901/Planium-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use valentin0901/Planium-2B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="valentin0901/Planium-2B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("valentin0901/Planium-2B")
model = AutoModelForMultimodalLM.from_pretrained("valentin0901/Planium-2B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use valentin0901/Planium-2B 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 valentin0901/Planium-2B
# Run inference directly in the terminal:
llama cli -hf valentin0901/Planium-2B

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf valentin0901/Planium-2B
# Run inference directly in the terminal:
llama cli -hf valentin0901/Planium-2B

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 valentin0901/Planium-2B
# Run inference directly in the terminal:
./llama-cli -hf valentin0901/Planium-2B

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 valentin0901/Planium-2B
# Run inference directly in the terminal:
./build/bin/llama-cli -hf valentin0901/Planium-2B

Use Docker

docker model run hf.co/valentin0901/Planium-2B

LM Studio
Jan

vLLM

How to use valentin0901/Planium-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "valentin0901/Planium-2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "valentin0901/Planium-2B",
		"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/valentin0901/Planium-2B

SGLang

How to use valentin0901/Planium-2B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "valentin0901/Planium-2B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "valentin0901/Planium-2B",
		"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 images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "valentin0901/Planium-2B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "valentin0901/Planium-2B",
		"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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use valentin0901/Planium-2B with Ollama:
```
ollama run hf.co/valentin0901/Planium-2B
```
Unsloth Desktop

How to use valentin0901/Planium-2B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf valentin0901/Planium-2B

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": "valentin0901/Planium-2B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use valentin0901/Planium-2B with Docker Model Runner:
```
docker model run hf.co/valentin0901/Planium-2B
```

Lemonade

How to use valentin0901/Planium-2B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull valentin0901/Planium-2B

Run and chat with the model

lemonade run user.Planium-2B-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use valentin0901/Planium-2B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf valentin0901/Planium-2B

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 valentin0901/Planium-2B

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use valentin0901/Planium-2B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf valentin0901/Planium-2B

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 "valentin0901/Planium-2B" \
  --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"

Planium-2B

Planium-2B is a 2B fine-tune of Qwen3.5 specialized in automated planning and its standard notation, PDDL (Planning Domain Definition Language). It is built primarily for robotics and other control systems.

In its 4-bit quantized build, it is small enough to run on an ordinary PC or an embedded board such as an NVIDIA Jetson — no cloud API, nothing leaves your machine.

What it does

Classical planning in PDDL, largely limited to the :strips and :strips :typing fragments:

turning a plain-language request into PDDL — typically the :goal or the problem file for a given domain,
reading and explaining an existing domain, problem or plan,
reasoning about states, preconditions, effects and goals,
spotting what is wrong in a piece of PDDL,
calling tools: it is trained to work in an agentic loop and to emit tool calls, so it can drive a parser, a planner or a plan validator itself instead of only producing text.

Temporal planning, numeric fluents and the richer ADL constructs are much less well supported.

The fine-tuning also mitigates a failure mode of the base Qwen3.5 on these tasks: reasoning that never terminates, or that drags on far longer than the problem warrants without getting closer to an answer. Planium-2B keeps its reasoning short and commits to an answer.

Files

File	Description
`model.safetensors-00001-of-00001.safetensors`	Full weights (bfloat16)
`gguf/model.gguf`	Quantized build for llama.cpp (Q4_K_M, ~1.3 GB)

Usage

llama.cpp

Download gguf/model.gguf and point llama-cli at it:

llama-cli -m /path/to/model.gguf

Or serve it over an OpenAI-compatible API:

llama-server -m /path/to/model.gguf --port 8080

Transformers

from transformers import AutoModelForImageTextToText, AutoTokenizer

model_id = "valentin0901/Planium-2B"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(model_id, dtype="bfloat16", device_map="auto")

messages = [{"role": "user", "content": "Write a STRIPS blocksworld domain."}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=1024)
print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

Built for NeuroPlan

Planium-2B is the model behind NeuroPlan, a neuro-symbolic planning system that bridges large language models and symbolic planners: it turns a request into PDDL, hands it to the planner and runs the resulting plan.