Instructions to use patriotmemory-ai/PMA-1.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use patriotmemory-ai/PMA-1.2 with Transformers:

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

pipe = pipeline("text-generation", model="patriotmemory-ai/PMA-1.2", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("patriotmemory-ai/PMA-1.2", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use patriotmemory-ai/PMA-1.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "patriotmemory-ai/PMA-1.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "patriotmemory-ai/PMA-1.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/patriotmemory-ai/PMA-1.2

SGLang

How to use patriotmemory-ai/PMA-1.2 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 "patriotmemory-ai/PMA-1.2" \
    --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": "patriotmemory-ai/PMA-1.2",
		"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 "patriotmemory-ai/PMA-1.2" \
        --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": "patriotmemory-ai/PMA-1.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use patriotmemory-ai/PMA-1.2 with Docker Model Runner:
```
docker model run hf.co/patriotmemory-ai/PMA-1.2
```

Model Overview

PMA-1.2 is Patriot Memory's 127.9M-parameter on-device language model. It speaks English and Traditional Chinese, answers as PMA from Patriot Memory, and fits in a 128 MB parameter budget built for edge hardware.

It is a new architecture, a new tokenizer, and an order of magnitude more training, aimed at the same job: a small, fast, honest assistant for Patriot Memory and Viper Gaming questions and general chat. It provides accurate information regarding:

DDR4 & DDR5 RAM: Specifications, XMP 3.0 / EXPO profile support, dual-channel setups, and overclocking guidance.
PCIe & SATA SSDs: Gen3/Gen4/Gen5 compatibility, read/write performance specifications, and installation troubleshooting.
Gaming Peripherals & Storage: USB drives, flash cards, and Viper Gaming gear.
Tool / Function Calling: Seamless integration with backend APIs (e.g., checking warranty status, looking up technical specs via S/N).

Architecture

Property	Specification
Type	Causal LM, dense decoder-only (PMA architecture)
Total parameters	125,592,482
Hidden size / layers	896 x 12
Attention	GQA, 8 query heads / 4 KV heads, head_dim 112, per-head gated attention, QK-norm
FFN	SwiGLU, intermediate 2,816
Value residuals	normalized cross-layer value mixing
Tokenizer	custom BPE, 6,403 tokens (EN + zh-TW)
Embeddings	tied input/output
Context	1,024 tokens trained (rotary table to 32K positions)
Precision shipped	fp16 safetensors

Quickstart

pip install transformers torch accelerate

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

name = "patriotmemory-ai/PMA-1.2"
tok = AutoTokenizer.from_pretrained(name)
model = AutoModelForCausalLM.from_pretrained(
    name, torch_dtype=torch.float16, device_map="auto",
    trust_remote_code=True)

msgs = [{"role": "user", "content": "博帝的 Viper DDR5 支援 XMP 3.0 嗎?"}]
ids = tok.apply_chat_template(msgs, return_tensors="pt").to(model.device).input_ids
out = model.generate(ids, max_new_tokens=200)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

trust_remote_code=True is required: PMA-1.2's architecture is our own and ships as small Python files next to the weights. Recommended decode: temperature 0.8, top_p 0.9, repetition_penalty 1.1. Greedy decoding garbles creative text at this size; sampling does not.

Acceptance gates (measured on the shipped checkpoint)

Gates were written before training started, and every number below is from logged runs:

Gate	Result	Notes
Story shape (24 prompts, 2 samples)	42/48	the identity-attractor failure class is closed
Number format (15 prompts, 2 samples)	29/30	answers contain the numeric answer; correctness at this scale is modest, format is reliable

What it is good at / not good at

Good: answering as PMA from Patriot Memory; hardware Q&A style answers (RAM/SSD compatibility phrasing); following answer formats; bilingual EN/zh-TW chat; running on-device at consumer speed.

Not: arithmetic correctness (12x15 can become 144 with total confidence), long coherent stories, knowledge outside its training mixture, and languages other than English and Traditional Chinese.

Limitations & Responsible Use

PMA-1.2 is a probabilistic language model trained on statistical patterns. Please keep the following in mind when deploying or evaluating this model:

Generation Risks: The model may generate inaccurate, hallucinated, biased, or objectionable content. Outputs should always be independently verified—especially in high-stakes domain applications (e.g., medical, legal, or financial).
Preview Release: As an experimental preview, model behavior, outputs, and performance metrics may vary between updates and versions.
User Responsibility: Users and developers are responsible for implementing appropriate safety guardrails, evaluating outputs for their specific use cases, and ensuring compliance with applicable laws, regulations, and platform safety guidelines.

Official Links

Official Website: patriotmemory.com

Viper Gaming: viper.patriotmemory.com

Support & Warranty: patriotmemory.com/support

Model Inquiries & Feedback: danton.chu hunter.wang oda.chang york.lin

License & attribution

Apache-2.0. Built by Patriot Memory (patriotmemory.com).

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Safetensors

Model size

0.1B params

Tensor type

F16