---
language:
- or
license: other
license_name: research-only
base_model:
- sarvamai/sarvam-1
- google/gemma-4-E4B-it
library_name: transformers
tags:
- odia
- checkpoints
---

# Odia tutor E07: the training checkpoints

Every checkpoint of experiment E07's two fine-tuning runs, kept so any evaluation in the E07 report can be
recomputed. The final models, the comparison and the model card are in
[fastpixels/odia-tutor-e07](https://huggingface.co/fastpixels/odia-tutor-e07). **Private, research use only.**

| Folder | What | Size each |
|---|---|---|
| `sarvam1-sft/checkpoint-1500`, `-3000`, `-4272` | Sarvam-1 trainer checkpoints: fp32 weights, tokenizer, `trainer_state.json` (the full log history) | 10.1 GB |
| `gemma4-e4b-sft/checkpoint-1500`, `-3000`, `-4272` | Gemma-4-E4B-it trainer checkpoints: trained weights in fp32, frozen parts (per-layer embeddings, vision, audio) in bf16 | about 27 GB |
| `gemma4-e4b-sft/step-800-bf16`, `-1600-bf16`, `-2300-bf16` | Gemma snapshots at the steps matched to Sarvam-1's checkpoints (same tokens seen, about the same hours): bf16 weights and tokenizer | 17.3 GB |

Only the newest checkpoint of each run keeps its resume state (`optimizer.pt` with AdamW's moments, `scheduler.pt`,
`rng_state.pth`): it is uploaded with every checkpoint while training runs, as a failsafe, and removed from the
older one once a newer checkpoint has its own. At the end, `checkpoint-4272` keeps it (Sarvam-1 20.2 GB, Gemma
about 37 GB), so either run can be continued exactly. Each run is 4,272 steps: two passes over 136,664 rows in
batches of 64.

## Use

Download one folder, never the whole repo (a few hundred GB):

```python
from huggingface_hub import snapshot_download
from transformers import AutoModelForCausalLM, AutoTokenizer
repo, sub = "fastpixels/odia-tutor-e07-checkpoints", "sarvam1-sft/checkpoint-3000"
path = snapshot_download(repo, allow_patterns=f"{sub}/*", ignore_patterns=["*optimizer.pt"])
model = AutoModelForCausalLM.from_pretrained(f"{path}/{sub}", dtype="bfloat16")
tok = AutoTokenizer.from_pretrained(f"{path}/{sub}")
```

To continue training from the last checkpoint, download `checkpoint-4272` with its `optimizer.pt` into the run's
output folder and resume (`odia-train ... --resume`, or `Trainer.train(resume_from_checkpoint=...)`). The cosine
schedule ended at step 4,272, so going further needs a new schedule.

## Licence

`sarvam1-sft/`: the Sarvam AI Research License (non-commercial), inherited from Sarvam-1. `gemma4-e4b-sft/`: base
model Apache 2.0; research use only because of the fine-tuning data (SciQ is CC BY-NC 3.0).