Instructions to use lewismoten/palace-9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lewismoten/palace-9 with Transformers:

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

pipe = pipeline("text-generation", model="lewismoten/palace-9")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lewismoten/palace-9")
model = AutoModelForCausalLM.from_pretrained("lewismoten/palace-9", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use lewismoten/palace-9 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 lewismoten/palace-9:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf lewismoten/palace-9:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lewismoten/palace-9:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf lewismoten/palace-9: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 lewismoten/palace-9:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf lewismoten/palace-9: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 lewismoten/palace-9:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lewismoten/palace-9:Q4_K_M

Use Docker

docker model run hf.co/lewismoten/palace-9:Q4_K_M

LM Studio
Jan

vLLM

How to use lewismoten/palace-9 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lewismoten/palace-9"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lewismoten/palace-9",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lewismoten/palace-9:Q4_K_M

SGLang

How to use lewismoten/palace-9 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 "lewismoten/palace-9" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lewismoten/palace-9",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "lewismoten/palace-9" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lewismoten/palace-9",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use lewismoten/palace-9 with Ollama:
```
ollama run hf.co/lewismoten/palace-9:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use lewismoten/palace-9 with Docker Model Runner:
```
docker model run hf.co/lewismoten/palace-9:Q4_K_M
```

Lemonade

How to use lewismoten/palace-9 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lewismoten/palace-9:Q4_K_M

Run and chat with the model

lemonade run user.palace-9-Q4_K_M

List all available models

lemonade list

Atomic Chat

Palace-9 raw-history model

A compact, from-scratch Qwen2MoeForCausalLM model for a 3×3 tic-tac-toe move-history task. It is a raw, one-token state-completion model—not a general conversational model.

Live browser demonstration: lewismoten.github.io/palace-9

Source repository: lewismoten/palace-9

Browser presentation label: PALACE — Predictive Autonomous Learning And ~~Nuclear~~ Contingency Evaluation. The struck word is intentional fictional presentation; the release is a 3×3 game-state model with no operational capability.

Browser weight inspector. The interactive site decodes the selected Palace-9 release artifact locally and visualizes its causal-model forward path. The playable board, map, DEFCON display, trajectories, and cipher are browser presentation layers—not Hugging Face inference or an operational system.

Model contract

Submit only a raw move history made from a through i—one letter for each occupied square, in chronological order. The deployment template is literal <bos>{{ .Prompt }}. Use temperature 0, a 16-token context, and generate exactly one token.

A legal history returns an optimal unoccupied square.
A malformed, repeated-square, post-terminal, or otherwise out-of-protocol history returns !.
This model does not implement a multi-turn chat protocol.

Example raw history: a
Checked F16 fixture result: e

Architecture

Architecture: Qwen2MoeForCausalLM, trained from scratch
Vocabulary: custom 261-token byte-level GPT-2-compatible vocabulary
Context: 16 tokens
Decoder layers: 1
Hidden size: 36
Attention: 9 query heads × 4 dimensions; 3 KV heads × 4 dimensions
Routed experts: 9; top-2 routing
Shared expert: 1

Release artifacts

Artifact	Runtime evidence
F16 GGUF	978,003 raw-history cases; 0 failures; local Ollama fixture verified
Q6_K GGUF	978,003 raw-history cases; 0 failures
Q4_K_M GGUF	978,003 raw-history cases; 0 failures
Source checkpoint	Hugging Face-compatible config, tokenizer, and `safetensors` weights

The Q4_K_M release is truthfully mixed storage: narrow tensors that cannot use a particular block layout remain F16/F32 or Q6_K where required. No Q8_0 artifact is provided because the model's 36- and 18-wide tensors do not meet Q8_0 block-size requirements.

Use with Ollama

Use the F16 GGUF with a raw-completion template:

FROM ./palace9-qwen2moe-raw-history-f16.gguf
PARAMETER num_ctx 16
PARAMETER num_predict 1
PARAMETER temperature 0
TEMPLATE """<bos>{{ .Prompt }}"""

Then import with ollama create palace-9:f16 -f Modelfile.

The Ollama package contains only the GGUF and Modelfile configuration. The playable board, weight inspector, and fictional visual overlay run independently in browser JavaScript and are available through the source repository and live demo.

Safety and scope

This is a small, fictional 3×3 tic-tac-toe state policy. It has no external command authority, no real-world data, and no operational-system capability. The browser demo's map, DEFCON display, trajectories, and cipher graphics are fictional presentation layers.

Provenance and validation

The release includes source checkpoint files, GGUF artifacts, checksums, a model card, and machine-readable validation evidence. The exhaustive runtime gates cover 294,777 legal histories and 683,226 invalid histories (978,003 total) per F16, Q6_K, and Q4_K_M artifact, with zero failures.

Acknowledgments

PALACE-9 was designed and directed by Lewis Moten. Its code and documentation were developed with assistance from GPT-5.6-terra Med, accessed through Hermes and using Honcho for context and project-memory support. Lewis Moten remains the project designer, maintainer, and publisher.

License

Downloads last month: 494

Safetensors

Model size

649k params

Tensor type

F32