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yonetix/Yonetix-V1
Yonetix-V1 is a machine learning model from yonetix. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
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From the Hugging Face model README
广州柚鸟科技有限公司旗下 YONETIX 品牌首款大语言模型
Yonetix-V1 是基于 Decoder-only Transformer 架构从零训练的大语言模型,113.5M 参数,支持中英文对话。
| 项目 | 规格 |
|---|---|
| 参数量 | 113.5M |
| 架构 | Decoder-only Transformer + RoPE + SwiGLU |
| 词表 | 367 tokens(字符级,支持中英文+标点+数字) |
| 训练数据 | 5万条合成语料,200+主题模板 |
| 训练硬件 | NVIDIA RTX 3060 12GB |
| 模型大小 | ~434MB (FP32) |
| 文件 | 说明 |
|---|---|
model.pt | 模型权重(PyTorch state_dict) |
config.json | 模型配置 |
vocab.txt | 词表(\t 分隔:index\ttoken) |
model.py | 模型架构代码 |
use_model.py | 快速使用脚本 |
import torch
import json
from model import YonetixTransformer, ModelConfig
# 加载配置和模型
with open("config.json") as f:
config = ModelConfig(**json.load(f))
model = YonetixTransformer(config)
model.load_state_dict(torch.load("model.pt", map_location="cpu", weights_only=True))
model.eval()
print(f"✅ 模型加载完成,参数量: {sum(p.numel() for p in model.parameters()) / 1e6:.2f}M")
# 加载词表
vocab = {}
with open("vocab.txt") as f:
for line in f:
idx, token = line.strip().split("\t")
vocab[int(idx)] = token
# 特殊 token
PAD, UNK, BOS, EOS = 0, 1, 2, 3
def encode(text, max_len=128):
"""编码文本到 token ids"""
ids = [BOS]
for ch in text:
found = False
for idx, token in vocab.items():
if idx < 4: # 跳过特殊 token
continue
if token == ch:
ids.append(idx)
found = True
break
if not found:
ids.append(UNK)
ids.append(EOS)
return torch.tensor([ids[:max_len]])
def decode(ids):
"""解码 token ids 到文本"""
return "".join(vocab.get(int(i), "") for i in ids if int(i) > 3)
# 对话
def chat(prompt, max_new_tokens=128):
input_ids = encode(prompt)
output_ids = model.generate(input_ids, max_new_tokens=max_new_tokens)
reply = output_ids[0][input_ids.size(1):].tolist()
return decode(reply)
print(chat("你好,你是谁?"))
YONETIX 是广州柚鸟科技有限公司旗下品牌,专注于全球实时数据可视化与人工智能技术研发。
官方网站: https://www.shhhh.cn/