Google's Gemma-2-2B — the raw pretrained checkpoint, before any instruction-tuning — completely unmodified. Note: every fine-tuned Gemma variant in this project (SFT, RQ, RAFT, DPO, RLAIF) was actually built on top of gemma-2-2b-it (Google's own instruction-tuned release), not this checkpoint — this page exists to show genuine sentence completion, which the instruction-tuned model resists even without a chat template applied.
| parameter | value | what it means |
|---|---|---|
| source | google/gemma-2-2b | pretrained only by Google, no instruction-tuning or RLHF applied — not the same checkpoint as gemma-2-2b-it, which every other Gemma variant in this project starts from |
| attention heads | 8 (GQA) | grouped-query attention, 4 KV heads |
| context length | 8,192 | far longer than any of the fine-tuned variants actually use |
| precision served | bf16 | no quantization on this demo specifically (the QLoRA variants load NF4) |
This page exists purely to give the base checkpoint the same kind of dedicated demo every fine-tuned variant has — nothing below reflects work done by this project on this specific model.
This is google/gemma-2-2b, Google's raw pretrained checkpoint — not gemma-2-2b-it. That distinction matters: an earlier version of this page served gemma-2-2b-it directly with no chat template, expecting it to behave like a base model, but it consistently refused to — even asked to continue an incomplete sentence, it responded in "assistant mode," asking for clarification instead of completing the text. That's a real, verified finding: RLHF/instruction-tuning generalized strongly enough in that checkpoint that removing the chat template alone wasn't sufficient to recover base-model completion behavior. Switching to the actual non-instruction-tuned checkpoint fixed it.
It has no legal/financial specialization and no exposure to the Q&A dataset every fine-tuned Gemma variant here was trained on — and unlike them, it isn't the starting point they were built from (they all start from gemma-2-2b-it). It's shown mainly as a reference point: compare its completions against the SLM-125M base demo (a true from-scratch base model) or against Gemma-2-2B-SFT to see what fine-tuning actually changed.