Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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Dataset Card for MiniModel Pretraining Corpus

This dataset is a curated, tokenized pretraining mixture designed specifically for training MiniModel-series small language models. It was tokenized using the Mistral-7B-Instruct-v0.3 tokenizer (vocab size: 32,768), which is included in the MiniModel-200M-Base repository.

For training code, data loading utilities, and full reproducibility (including the training script), see the official GitHub repository:
🔗 https://github.com/xTimeCrystal/MiniModel/tree/main

Dataset Details

This corpus combines high-quality educational and general-purpose text sources, filtered and balanced to maximize learning efficiency in low-compute training regimes.

Source Data Composition

The dataset is a weighted mixture of the following sources (by token count):

70% openbmb/Ultra-FineWeb (English subset)
20% openbmb/Ultra-FineWeb (Chinese subset)
5% Avelina/python-edu-cleaned
5% HuggingFaceTB/finemath

All source datasets are publicly available and compatible with the Apache 2.0 license.

Preprocessing

Tokenized with the Mistral-7B-Instruct-v0.3 tokenizer
Sequences were packed using a bin-packing algorithm to minimize padding (final padding < 5%)
Maximum sequence length: 2048 tokens
No deduplication beyond source-level filtering

💡 Note: The tokenizer, training configuration, and data-loading pipeline are provided in the GitHub repo for full reproducibility.

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Curated by:

xTimeCrystal

Languages:

English, Chinese, Python (code)

License:

Apache 2.0

Intended use:

Pretraining efficient small language models (e.g., MiniModel-200M-Base)