---
library_name: transformers
license: apache-2.0
base_model: allenai/Olmo-3-1025-7B
tags:
- axolotl
- generated_from_trainer
datasets:
- dataset-tfs-mk-IMP-SOS-processed-olmo3-think.jsonl
model-index:
- name: O37BB
  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: `0.13.0.dev0`
```yaml
# --- Base Model & Tokenizer Configuration ---
base_model: allenai/Olmo-3-1025-7B
trust_remote_code: true
hub_model_id: Auditt/O37BB          # Push the model to the Hugging Face Hub
chat_template_jinja: /workspace/data/model-output/chat_template.jinja    # Uses the template defined in tokenizer_config.json

# --- Dataset Configuration ---
# Assuming a standard conversation format (ShareGPT/ChatML style)
datasets:
  - path: dataset-tfs-mk-IMP-SOS-processed-olmo3-think.jsonl
    type: chat_template
    field_messages: messages    # The top-level key containing the list
    message_field_role: role            # The key inside the list for 'user'/'assistant'
    message_field_content: content      # The key inside the list for the actual text
    
    # 4. MAP YOUR ROLES
    # The keys (left) are what Axolotl expects. 
    # The values (right) are what exist in your raw JSONL file.
    roles:
      user: ["user"]
      assistant: ["assistant"]
      system: ["system"]
      
    # 5. SUPERVISION
    # This ensures loss is calculated ONLY on the "assistant" turns.
    roles_to_train: ["assistant"]
    
val_set_size: 0.1                   # 10% Validation, 90% Training
dataset_prepared_path: last_run_prepared

# --- Training Strategy ---
sequence_len: 60000                 # Max sequence length
sample_packing: true                # Efficiently packs samples to fill sequence_len
pad_to_sequence_len: true

# Supervision Settings
train_on_inputs: false              # False = Mask User prompts (Supervise Assistant only)
group_by_length: false              # Usually false when sample_packing is true

# --- Hyperparameters & Training Loop ---
num_epochs: 2
micro_batch_size: 1                 # Keep small due to 60k context
gradient_accumulation_steps: 4      # Adjust based on desired global batch size
learning_rate: 0.00001
optimizer: adamw_torch

# --- Distributed Training & Memory ---
context_parallel_size: 2            # Splits the 60k sequence across 2 GPUs
gradient_checkpointing: true        # Essential for 60k context
flash_attention: true               # Essential for speed/memory at this length

# --- Logging & Evaluation ---
logging_steps: 1                    # Log training loss every step
evals_per_epoch: 1                  # Run eval 1 times per epoch (roughly)
#eval_strategy: epoch
#save_strategy: epoch                # Save checkpoint at end of epoch
#wandb_project: olmo3-finetune       # Optional: Weights & Biases logging
#wandb_entity: your-entity           # Optional
output_dir: /workspace/data/model-output-base

# --- Precision ---
bf16: true                          # Bfloat16 is recommended for OLMo
fp16: false
tf32: true

tokens:                             # Add these to the tokenizer
  - "𐔲"
  - "𐔾"
  - "〉"
  - "𝜎"
  - "⋁"
  - "𐕠"
  - "𐕜"
  - "𐔸"
  - "∧"
  - "≥"
  - "𐕟"
  - "𐕖"
  - "⟂"
  - "𐕏"
  - "⋀"
  - "𐕣"
  - "𐕃"
  - "𐕙"
  - "𐕕"
  - "χ"
  - "𐕊"
  - "〈"
  - "𐕐"
  - "𐔻"
  - "𐕀"
  - "𐔳"
  - "≠"
  - "𐔷"
  - "≤"
  - "𐕞"
  - "𐔱"
  - "𐕂"
  - "↦"
  - "𐕎"
  - "→"
  - "𐕛"
  - "𐔰"
  - "ε"

```

</details><br>

# O37BB

This model is a fine-tuned version of [allenai/Olmo-3-1025-7B](https://huggingface.co/allenai/Olmo-3-1025-7B) on the dataset-tfs-mk-IMP-SOS-processed-olmo3-think.jsonl dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0019
- Memory/max Active (gib): 85.95
- Memory/max Allocated (gib): 82.72
- Memory/device Reserved (gib): 93.36

## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- total_eval_batch_size: 2
- 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: 10
- training_steps: 348

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Active (gib) | Allocated (gib) | Reserved (gib) |
|:-------------:|:------:|:----:|:---------------:|:------------:|:---------------:|:--------------:|
| No log        | 0      | 0    | 1.0680          | 58.72        | 55.5            | 65.44          |
| 0.0647        | 0.9943 | 174  | 0.0021          | 85.95        | 82.72           | 106.04         |
| 0.0296        | 1.9943 | 348  | 0.0019          | 85.95        | 82.72           | 93.36          |


### Framework versions

- Transformers 4.57.0
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1