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| 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) |
pip install transformers torch1import torch
2from transformers import AutoModelForCausalLM, AutoTokenizer
3
4model_name = "patriotmemory-ai/pma-1.0"
5
6tokenizer = AutoTokenizer.from_pretrained(model_name)
7model = AutoModelForCausalLM.from_pretrained(
8 model_name,
9 dtype="auto",
10 device_map="auto"
11)
12
13prompt = "博帝的 Viper Venom DDR5 記憶體支援 XMP 3.0 嗎?"
14messages = [
15 {"role": "system", "content": "你是 Patriot Memory（博帝科技）的官方智能客服小幫手。"},
16 {"role": "user", "content": prompt}
17]
18
19text = tokenizer.apply_chat_template(
20 messages,
21 tokenize=False,
22 add_generation_prompt=True
23)
24
25model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
26model_inputs.pop("token_type_ids", None)
27
28generated_ids = model.generate(
29 **model_inputs,
30 max_new_tokens=512
31)
32
33input_len = model_inputs["input_ids"].shape[1]
34generated_tokens = generated_ids[0][input_len:]
35
36response = tokenizer.decode(generated_tokens, skip_special_tokens=True)
37print(response)