fb752d0aab
Model: Raghav-Singhal/epe-1p-smollm-1p7b-100B-20n-2048sl-960gbsz-bce Source: Original Platform
43 lines
1.1 KiB
Markdown
43 lines
1.1 KiB
Markdown
---
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- llama
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- causal-lm
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- bfloat16
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---
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# epe-1p-smollm-1p7b-100B-20n-2048sl-960gbsz-bce
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Converted Hugging Face base checkpoint from the Model Raising EPE pretraining run.
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## Details
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- Architecture: `LlamaForCausalLM`
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- Base model size: `1.7B`
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- Precision on disk: `bfloat16`
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- Source Megatron checkpoint: `iter_0050863`
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- Tokenizer: extended SmolLM2 tokenizer with 36 additional special tokens (`<assistant>` + 35 `<charter_X.Y>` tokens)
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- Config vocab size: `49280` padded rows
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- Tokenizer length: `49188`
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## Variant
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This is the `1p` EPE variant trained with BCE constitution-prediction loss.
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## Chat Templates
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Two named chat templates are provided:
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| Name | Use case |
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|------|----------|
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| `default` | Standard chat format with the plain `assistant` role |
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| `epe` | Uses `<assistant>` at the start of assistant turns |
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```python
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tok.apply_chat_template(messages, chat_template="default")
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tok.apply_chat_template(messages, chat_template="epe")
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```
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Always use the bundled tokenizer; the original SmolLM2 tokenizer has only 49152 tokens and will not cover the EPE special tokens.
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