---
library_name: transformers
tags:
- axolotl
- generated_from_trainer
datasets:
- ChaoticNeutrals/Luminous_Opus
- ChaoticNeutrals/Synthetic-Dark-RP
- ChaoticNeutrals/Synthetic-RP
model-index:
- name: Tiny-Darkllama3.2-1B-Instruct
results: []
base_model:
- unsloth/Llama-3.2-1B
---
[
](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config
axolotl version: `0.6.0`
```yaml
base_model: unsloth/Llama-3.2-1B
bf16: false
dataset_prepared_path: last_run_prepared
datasets:
- chat_template: alpaca
field_messages: conversations
message_field_content: value
message_field_role: from
path: ChaoticNeutrals/Luminous_Opus
split: train
type: chat_template
debug: null
deepspeed: null
early_stopping_patience: null
evals_per_epoch: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
group_by_length: false
hub_model_id: mrcuddle/Tiny-Darkllama3.2-1B-Instruct
is_llama_derived_model: true
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lr_scheduler: linear
max_steps: 20
micro_batch_size: 1
mlflow_experiment_name: colab-example
model_type: LlamaForCausalLM
num_epochs: 4
optimizer: adamw_torch
output_dir: ./llama2
pad_to_sequence_len: true
resume_from_checkpoint: null
sample_packing: true
saves_per_epoch: null
sequence_len: 1096
special_tokens: null
strict: false
tf32: false
tokenizer_type: LlamaTokenizer
train_on_inputs: false
wandb_entity: null
wandb_log_model: null
wandb_name: null
wandb_project: null
wandb_watch: null
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
# Tiny-Darkllama3.2-1B-Instruct
This model was trained from unsloth/Llama-3.2-1B on the ChaoticNeutrals/Luminous_Opus, Synthetic-Dark-RP, Synthetic-RP datasets.
## Training and evaluation data
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- 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: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 20
### Training results
[2025-02-11 13:09:27,300] [INFO] [axolotl.train.train:173] [PID:7240] [RANK:0] Starting trainer...
[2025-02-11 13:09:27,706] [INFO] [axolotl.utils.samplers.multipack.calc_min_len:203] [PID:7240] [RANK:0] gather_len_batches: [35]
[2025-02-11 13:09:27,761] [INFO] [axolotl.callbacks.on_train_begin:39] [PID:7240] [RANK:0] The Axolotl config has been saved to the MLflow artifacts.
{'loss': 3.4922, 'grad_norm': 9.877531051635742, 'learning_rate': 2e-05, 'epoch': 0.03}
5% 1/20 [00:02<00:37, 1.98s/it][2025-02-11 13:09:31,221] [INFO] [axolotl.callbacks.on_step_end:127] [PID:7240] [RANK:0] cuda memory usage while training: 12.320GB (+8.604GB cache, +0.565GB misc)
{'loss': 3.3057, 'grad_norm': 11.661816596984863, 'learning_rate': 4e-05, 'epoch': 0.06}
{'loss': 2.4733, 'grad_norm': 8.751928329467773, 'learning_rate': 6e-05, 'epoch': 0.09}
{'loss': 2.9842, 'grad_norm': 10.503549575805664, 'learning_rate': 8e-05, 'epoch': 0.11}
{'loss': 2.6624, 'grad_norm': 12.645892143249512, 'learning_rate': 0.0001, 'epoch': 0.14}
{'loss': 2.7616, 'grad_norm': 10.691230773925781, 'learning_rate': 0.00012, 'epoch': 0.17}
{'loss': 2.9891, 'grad_norm': 10.076760292053223, 'learning_rate': 0.00014, 'epoch': 0.2}
{'loss': 2.3745, 'grad_norm': 10.034379959106445, 'learning_rate': 0.00016, 'epoch': 0.23}
{'loss': 2.4965, 'grad_norm': 9.778562545776367, 'learning_rate': 0.00018, 'epoch': 0.26}
{'loss': 2.3811, 'grad_norm': 19.146963119506836, 'learning_rate': 0.0002, 'epoch': 0.29}
{'loss': 3.3611, 'grad_norm': 14.556534767150879, 'learning_rate': 0.00018, 'epoch': 0.31}
{'loss': 2.9619, 'grad_norm': 16.88424301147461, 'learning_rate': 0.00016, 'epoch': 0.34}
{'loss': 2.121, 'grad_norm': 9.94941520690918, 'learning_rate': 0.00014, 'epoch': 0.37}
{'loss': 2.1042, 'grad_norm': 23.178285598754883, 'learning_rate': 0.00012, 'epoch': 0.4}
{'loss': 2.4722, 'grad_norm': 10.403461456298828, 'learning_rate': 0.0001, 'epoch': 0.43}
{'loss': 2.7434, 'grad_norm': 11.339975357055664, 'learning_rate': 8e-05, 'epoch': 0.46}
{'loss': 2.2349, 'grad_norm': 202.98793029785156, 'learning_rate': 6e-05, 'epoch': 0.49}
{'loss': 2.3479, 'grad_norm': 10.250885009765625, 'learning_rate': 4e-05, 'epoch': 0.51}
{'loss': 2.4169, 'grad_norm': 14.021651268005371, 'learning_rate': 2e-05, 'epoch': 0.54}
{'loss': 3.4686, 'grad_norm': 10.988056182861328, 'learning_rate': 0.0, 'epoch': 0.57}
{'train_runtime': 172.0118, 'train_samples_per_second': 0.116, 'train_steps_per_second': 0.116, 'train_loss': 2.707640600204468, 'epoch': 0.57}
100% 20/20 [02:52<00:00, 8.65s/it]
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0