Instructions to use PocketBrains233/PocketPlanning-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PocketBrains233/PocketPlanning-1 with Transformers:

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

pipe = pipeline("text-generation", model="PocketBrains233/PocketPlanning-1")
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("PocketBrains233/PocketPlanning-1")
model = AutoModelForMultimodalLM.from_pretrained("PocketBrains233/PocketPlanning-1")
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

vLLM

How to use PocketBrains233/PocketPlanning-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PocketBrains233/PocketPlanning-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PocketBrains233/PocketPlanning-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PocketBrains233/PocketPlanning-1

SGLang

How to use PocketBrains233/PocketPlanning-1 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 "PocketBrains233/PocketPlanning-1" \
    --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": "PocketBrains233/PocketPlanning-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "PocketBrains233/PocketPlanning-1" \
        --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": "PocketBrains233/PocketPlanning-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PocketBrains233/PocketPlanning-1 with Docker Model Runner:
```
docker model run hf.co/PocketBrains233/PocketPlanning-1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

PocketPlaning-1

PocketPlaning-1 is a specialized AI agent planning model built on Qwen3.5-27B. It is fine-tuned for intelligent task routing, complexity estimation, and tool-calling orchestration in agentic workflows.

Key Capabilities

Task Planning: Decomposes complex tasks into ordered subtasks with dependency tracking
Complexity Routing: Classifies subtasks as EASY (handle locally) or HARD (escalate to frontier model)
Tool Calling: Native function-calling support for agentic tool orchestration
Bilingual: Full support for English and Chinese

Model Details

Attribute	Value
Base Model	Qwen3.5-27B
Parameters	27B
Context Length	262,144 tokens
Architecture	Qwen3.5ForConditionalGeneration
Precision	bfloat16
License	Qwen License

Usage

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "PocketBrains/PocketPlaning-1"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="bfloat16",
    device_map="auto",
    trust_remote_code=True,
)

messages = [{"role": "user", "content": "Help me write a Python script to parse CSV files"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=2048)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

With vLLM

vllm serve PocketBrains/PocketPlaning-1 \
    --tensor-parallel-size 2 \
    --max-model-len 131072 \
    --dtype bfloat16 \
    --trust-remote-code \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_coder

Intended Use

PocketPlaning-1 is designed to serve as the planning and routing layer inside AI agent systems. It determines what to do next and how much compute to spend doing it, enabling cost-efficient agent execution by routing simple tasks locally and escalating complex tasks to frontier models.

Limitations

This model is optimized for agent planning and tool-calling scenarios; it is not a general-purpose chatbot.
Performance is best when used within an agentic framework with tool definitions provided.
The model inherits limitations from the base Qwen3.5-27B model.

Citation

@misc{pocketplan2026,
  title={PocketPlaning-1: Efficient Planning Models for AI Agents},
  author={PocketBrains Inc.},
  year={2026},
  url={https://huggingface.co/PocketBrains/PocketPlaning-1}
}

About PocketBrains

PocketBrains Inc. builds open-source efficient models for AI agents. We train specialized small models that make frontier model usage economically viable by intelligently routing tasks based on complexity.

Website: pocketbrainsai.com
Contact: founder@pocketbrainsai.com

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Safetensors

Model size

3.05M params

Tensor type

BF16

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Quantizations

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