PMA-1.2

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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).

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Architecture

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)
out = model.generate(ids, max_new_tokens=200)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

If you run into multi-GPU tensor device mismatch errors: RuntimeError: Expected all tensors to be on the same device... Run the script with CUDA_VISIBLE_DEVICES=0 to isolate execution to GPU 0.

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.

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Acceptance gates (measured on the shipped checkpoint)

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

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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.

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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.

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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

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License & attribution

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