patriotmemory-ai/PMA-1.0

Patriot Memory AI Model

Welcome to the official repository for the Patriot Memory AI Model. Developed by Patriot Memory (patriotmemory.com), this lightweight, fine-tuned language model is optimized for hardware domain knowledge, customer support, compatibility checks, and function/tool calling.

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Model Overview

PMA-1.0 is a lightweight language model based on the MiniMind architecture, designed for fast inference and efficient deployment.

This model serve as an intelligent representative for Patriot Memory and its premium gaming brand, Viper Gaming. 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).

Model Information

Property	Specification
Model Type	Causal Language Model (Dense Decoder-Only)
Total Parameters	~63.9 Million (0.064B)
Context Length	32,768 tokens (32K)
Hidden Size (Embedding)	768
Intermediate Size (FFN)	2,432
Attention Heads	8 Query heads (4 KV heads)
Num Layers	8 Transformer blocks
Primary Language	Traditional Chinese (zh-TW) / English (en)

Quickstart Guide

1. Installation Ensure you have transformers and torch installed:

pip install transformers torch

2. Inference Code

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "patriotmemory-ai/pma-1.0"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    dtype="auto",
    device_map="auto"
)

prompt = "博帝的 Viper Venom DDR5 記憶體支援 XMP 3.0 嗎?"
messages = [
    {"role": "system", "content": "你是 Patriot Memory（博帝科技）的官方智能客服小幫手。"},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
model_inputs.pop("token_type_ids", None)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)

input_len = model_inputs["input_ids"].shape[1]
generated_tokens = generated_ids[0][input_len:]

response = tokenizer.decode(generated_tokens, skip_special_tokens=True)
print(response)

Limitations & Responsible Use

PMA-1.0 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@patriotmemory.com hunter.wang@patriotmemory.com oda.chang@patriotmemory.com york.lin@patriotmemory.com

License

This model card and associated weights are released under the Apache 2.0 License.