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Mistral-Small-24B-Instruct-2501 (2025)

Mistral-Small-24B-Instruct-2501 is a large language model released by Mistral AI, released under the apache-2.0 license, with a claimed knowledge cutoff of 2025-01-30. Its weights, configuration, and tokenizer files have been cryptographically hashed and are queued for on-chain attestation by Weight Registry.

Description

Mistral Small 3 is a 24B-parameter language model from Mistral AI, released January 2025 and optimized for low-latency performance across common tasks. Released under Apache 2.0, it includes both pretrained and instruction-tuned checkpoints.

Metadata

Type
LLM
Issuer
Mistral AI
License
apache-2.0
Size
43.91 GB
Knowledge cutoff
2025-01-30
Source
https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501
Revision
9527884be6e5616bdd54de542f9ae13384489724

Hugging Face stats

52,250 downloads on Hugging Face · 970 likes · library: vllm

vllmsafetensorsmistralenfrdeesitptzh

Weight Hashes

Weights file
10 shards (Merkle root) · 43.91 GB
SHA-25640f077507839a65f5ef0885f71ea83550cfe8262941c537f0102f4ee3c5b94a3
SHA3-256a474b1820b968cebc21fca43c671f2976f0b4c58b0182054c42ff93e2d88cd81
BLAKE3e55826b55ea5105e344747dd942f499bf8e87e731043dd546148db458f5180ea
Config file
config.json · 623 B
SHA-256ff5f8d1747db4e16def20db719d867e74d0b764330249357351b2b44520b8001
SHA3-256b78f49d3218f4a2e98a59753164b009e43915d6ff5a8f64b5f9ca56b2a8d8f51
BLAKE30f70356f36ea77a66231df95d554c319667227429520dc83a2e3ab1100059538
Tokenizer file
tokenizer.json · 16.29 MB
SHA-256b76085f9923309d873994d444989f7eb6ec074b06f25b58f1e8d7b7741070949
SHA3-2561ed6dc0b3e5fbb3c65ea19d443a8dd1843016489e35eb426b3759db943c55a58
BLAKE3a43646ba93f3e4a1e2d345013db3042f553ab6486b349c5389b4445e1b06e793

Verify it yourself

Download the file(s) from the source below, then hash them locally (sha256sum <file> on Linux/macOS, certutil -hashfile <file> SHA256 on Windows) and compare the output against the hash(es) shown above.

Anomalies Flagged

  • other weight-like files present but not hashed: ['consolidated.safetensors']