See axolotl config

axolotl version: 0.6.0

adapter: lora
base_model: NousResearch/Llama-3.2-1B
bf16: auto
dataset_prepared_path: last_run_prepared
datasets:
- path: teknium/GPT4-LLM-Cleaned
  type: alpaca
eval_sample_packing: true
evals_per_epoch: 4
flash_attention: true
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: false
hub_model_id: pandyamarut/llama-fr-lora
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_r: 16
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
loss_watchdog_patience: 3
loss_watchdog_threshold: 5
lr_scheduler: cosine
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_8bit
output_dir: /runpod-volume/fine-tuning/test-run
pad_to_sequence_len: true
run_name: test-run
runpod_job_id: dd327f42-5f67-4830-b512-4561fa9a3d45-u1
sample_packing: true
saves_per_epoch: 1
sequence_len: 2048
special_tokens:
  pad_token: <|end_of_text|>
strict: false
tf32: false
train_on_inputs: false
val_set_size: 0.1
wandb_entity: axo-test
wandb_name: test-run-1
wandb_project: test-run-1
warmup_steps: 10
weight_decay: 0

llama-fr-lora

This model is a fine-tuned version of NousResearch/Llama-3.2-1B on the teknium/GPT4-LLM-Cleaned dataset. It achieves the following results on the evaluation set:

• Loss: 1.1018

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: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 4
• 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: 10
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
1.4537	0.0009	1	1.3971
1.1978	0.2503	271	1.1561
1.1637	0.5007	542	1.1131
1.1894	0.7510	813	1.1018

Framework versions

• PEFT 0.14.0
• Transformers 4.47.1
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0