Pinyin-Code Causal LM
This repository contains a custom Transformers causal language model.
External evaluation repositories should load it with
trust_remote_code=True and use the causal backend.
Dependencies
Install the runtime dependencies before loading the model:
pip install torch transformers safetensors pypinyin jieba
This export uses a repository-native tokenizer and does not require SentencePiece. pypinyin is required
for raw Mandarin-to-pinyin preprocessing. jieba is required when
use_jieba is true; this export was created with use_jieba=true.
Loading
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForSequenceClassification,
AutoTokenizer,
)
model_path = "PATH_OR_REPO_ID"
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
base_model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
classifier = AutoModelForSequenceClassification.from_pretrained(
model_path,
trust_remote_code=True,
num_labels=3,
)
Evaluation
Configure external evaluators with:
- model path: this local folder or Hugging Face repo ID
- backend:
causal - trust remote code: enabled
The tokenizer accepts preprocessed pinyin-code text through standard
calls such as tokenizer(text), tokenizer(text, add_special_tokens=False),
and tokenizer(texts, padding=True, truncation=True, return_tensors="pt"). The model supports
output_hidden_states=True for representation extraction tasks.
This export sets patch_pathlib_utf8_open=true in config.json.
When loaded with trust_remote_code=True, the config installs a narrow
Windows compatibility shim so later text-mode Path.open("r") calls
without an explicit encoding default to UTF-8. Set
PINYIN_CODE_DISABLE_UTF8_OPEN_PATCH=1 before loading the model to
disable that shim.
Export metadata:
- tokenizer_kind:
atomic_bpe_cross - transliteration:
pinyin-code - use_jieba:
true