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timorobrecht
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Model Details
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timorobrecht
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Install the runtime dependencies before loading the model:
bash
pip install torch transformers safetensors sentencepiece pypinyin jieba
sentencepiece is required for the SentencePiece tokenizer. 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.
python
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,)
Configure external evaluators with:
causalThe tokenizer accepts raw text through standard calls such as
tokenizer(text), tokenizer(text, add_special_tokens=False), and
tokenizer(texts, padding=True, truncation=True, return_tensors="pt").
It also accepts return_offsets_mapping=True for compatibility with
completion-ranking evaluators that need suffix masks. 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:
sentencepiecepinyin-codetrue