Truncation support for recent Mistrals to prevent AsyncEngineDeadError on input exceeding max_model_len w/ chunked prefill

[nightflight-dk]

Contrary to other tokenizers, MistralTokenizer (as used by vLLM) always has the truncation config disabled, with upstream kwargs ignored. Crashing engines with excessive input can easily crash the background threads and lead to AsyncEngineDeadError in the foreground thread.

to reproduce, run vLLM with a recent Mistral, reduce the max_model_len and enable chunked prefill, then submit requests larger than 1000 tokens.

{
"model": "mistralai/Mistral-Small-24B-Instruct-2501",
"disable_log_requests": "true",
"gpu_memory_utilization": 0.9,
"max_model_len": 1000,
"tensor_parallel_size": 1,
"enable_chunked_prefill": "true",
"enable_lora": "true",
"max_num_seqs": 8
}

SOLUTION: tokenize with truncation before submitting - but MistralTokenizer as wrapped by vLLM needs to allow it.

FIX #5901

[github-actions]

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