eac123/Olmo3-32B-exp1b-e7

See axolotl config

axolotl version: 0.15.0

# ── Continued Pretraining: 32B on 8×A40 (48GB) ──
base_model: allenai/Olmo-3-1125-32B
tokenizer_type: AutoTokenizer

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

# ── Sequence / packing ──
sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true

# ── Batch sizing ──
micro_batch_size: 4
gradient_accumulation_steps: 4

# ── Training ──
train_on_inputs: true
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-4
warmup_ratio: 0.03
num_epochs: 50
weight_decay: 0.01

# ── Precision / memory ──
bf16: auto
tf32: true
flash_attention: true
gradient_checkpointing: true

# ── DeepSpeed ZeRO Stage 2 ──
deepspeed: train/axolotl-cpt/ds_32b.json

# ── Logging ──
logging_steps: 1
save_strategy: steps
save_steps: 10
save_total_limit: 40
use_wandb: true
wandb_name: "olmo3-32b-exp1b-lora"
wandb_project: "out-of-context-chatbots"

# ── QLoRA ──
load_in_8bit: false
load_in_4bit: false
adapter: lora
lora_r: 64
use_rslora: true
lora_alpha: 32
lora_target_linear: true
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj

model-out

This model is a fine-tuned version of allenai/Olmo-3-1125-32B 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: 0.0002
• 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_8BIT 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: 2
• training_steps: 50

Training results

Framework versions

• PEFT 0.18.1
• Transformers 5.3.0
• Pytorch 2.6.0+cu126
• Datasets 4.5.0
• Tokenizers 0.22.2