---
library_name: transformers
license: apache-2.0
base_model: allenai/Olmo-3-1025-7B
tags:
- generated_from_trainer
model-index:
- name: model-out
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.15.0`
```yaml
# ── Continued Pretraining: 7B on 8×A40 (48GB) ──

base_model: allenai/Olmo-3-1025-7B
tokenizer_type: AutoTokenizer

# ── Data ──
datasets:
  - path: data/1b/all.jsonl
    type: completion
    field: completion

# ── Sequence / packing ──
sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true
# NOTE: do NOT enable group_by_length with sample_packing

# ── Batch sizing ──
# Per-GPU: 4 seqs × 2048 tok = 8k tokens/step/GPU
# Global:  4 × 4 accum × 8 GPUs = 128 effective seqs/step
micro_batch_size: 4
gradient_accumulation_steps: 4

# ── Training ──
train_on_inputs: true
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 5e-5
warmup_steps: 200
max_steps: 150
weight_decay: 0.01

# ── Precision / memory ──
bf16: true
flash_attention: true
gradient_checkpointing: true

# ── DeepSpeed ZeRO Stage 2 ──
deepspeed: ds_stage2.json

# ── Logging ──
logging_steps: 10
save_strategy: steps
save_steps: 50
```

</details><br>

# model-out

This model is a fine-tuned version of [allenai/Olmo-3-1025-7B](https://huggingface.co/allenai/Olmo-3-1025-7B) on the data/1b/all.jsonl dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200
- training_steps: 150

### Training results



### Framework versions

- Transformers 5.3.0
- Pytorch 2.8.0+cu126
- Datasets 4.5.0
- Tokenizers 0.22.2