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The JWT signature verification failed. Check the signing key and the algorithm.
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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Canis.teach Math Dataset

Simple synthetic dataset for training Math tutoring models.

  • Project: Canis.teach - Learning that fits.
  • Subject: Math
  • Generated with: Canis.lab
  • Format: Simple ID:content pairs

Dataset Structure

{
  "id": "unique_identifier",
  "content": "tutoring conversation text"
}

This dataset contains educational conversations focused on Math topics, designed to teach effective tutoring behavior rather than just providing direct answers.

Usage

from datasets import load_dataset

dataset = load_dataset("CanisAI/teach-math-v1")

Content

The conversations emphasize:

  • Step-by-step explanations
  • Pedagogical guidance
  • Encouraging learning through hints
  • Subject-appropriate teaching methods

License

Apache 2.0

Related Models

Repository Description
CanisAI/teach-math-qwen3-4b-2507-r1 LoRA adapters
(Coming Soon) Merged model
(Coming Soon) GGUF quantized

Canis.teach - Learning that fits.

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