| xLLM Part II model artifacts |
| Towards Looped Models Done Right, Part II: Rethinking at Fixed Points |
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| The model weights are distributed under the Apache License, Version 2.0. |
| A copy is provided in LICENSE. The xLLM code has its own license in the |
| repository root; third-party code retains its original notices. |
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| Tokenizer attribution |
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| The tokenizer is the one the models were trained with: a BPE tokenizer with a |
| 64,000-token vocabulary that the authors' team trained on a Jais-style data |
| mix, which the paper calls Jais64k after Jais (Sengupta et al., 2023), with |
| added chat and tool special tokens. It is distributed with the weights under |
| the Apache License, Version 2.0, in tokenizer/: tokenizer.json, |
| tokenizer_config.json and special_tokens_map.json are copied unchanged. A |
| distilled student has no tokenizer/ and uses its teacher's. |
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| Artifact preparation: |
| - The trained FP32 tensors are rounded to BF16 and stored in Safetensors |
| shards, keyed as the xLLM model's state dict. |
| - config.json holds the release model config; for models with a tokenizer, its |
| tokenizer section points to tokenizer/, and for distilled students its student |
| section names the teacher artifact and its manifest SHA-256. |
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