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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tracktext

An experimental dataset that contains 128x64 greyscale images of TrackMania gameplay + keystrokes, designed for LLMs with 16k context or above.

Inspired by DOOM-Mistral-7b :)

Dataset Details

Format:

{data}
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
...
{action}
[0, 0, 0, 0]

Greyscale Precision: 1 decimal Capture rate: 6 frames per second

Uses

To train an LLM how to play TrackMania.

Direct Use

Specialized LLM

Out-of-Scope Use

Any regular LLM

Dataset Structure

Text files

Dataset Creation

Python script in repo

Curation Rationale

Self driving using an LLM? For fun

Source Data

TrackMania 2020 screengrabs

Personal and Sensitive Information

None

Bias, Risks, and Limitations

The resolution is not very high; there may be suboptimal results

Recommendations

Don't expect anything good

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