MODEL PROFILE

bytellama-43m-cc

mittagessen · bytellama-43m-cc

ByteLlama is a series of tiny byte-level language models built on the Llama architecture. Instead of a learned subword tokenizer, ByteLlama operates directly on raw UTF-8 bytes, using a simple octet tokenizer with a vocabulary size of 512. All models are trained on the **Historical and Cultural** subset of the Common Corpus, i.e. the open culture and pre-1900 filtered portion. Each model has been trained on 100 billion tokens (100,000 steps with an effective batch size of 512 and a sequence length of 2048). The models are trained with use in the party in mind. They expect a **language token** as the first input after the BOS token, indicating the language of the follow-up prompt. The tokenizer supports over 200 languages identified by ISO 639-3 codes. Given the distribution of languages in the training corpus it is unknown which languages the model has actually seen during training.

license:apache-2.0
Context windowNot disclosed
Input → Output待核验 → 待核验
Model weightsNot verified
Profile updated2026/11/2 19:17:03
OVERVIEW

Model overview

ByteLlama is a series of tiny byte-level language models built on the Llama architecture. Instead of a learned subword tokenizer, ByteLlama operates directly on raw UTF-8 bytes, using a simple octet tokenizer with a vocabulary size of 512. All models are trained on the **Historical and Cultural** subset of the Common Corpus, i.e. the open culture and pre-1900 filtered portion. Each model has been trained on 100 billion tokens (100,000 steps with an effective batch size of 512 and a sequence length of 2048). The models are trained with use in the party in mind. They expect a **language token** as the first input after the BOS token, indicating the language of the follow-up prompt. The tokenizer supports over 200 languages identified by ISO 639-3 codes. Given the distribution of languages in the training corpus it is unknown which languages the model has actually seen during training.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。