See axolotl config
axolotl version: 0.5.2
base_model: meta-llama/Llama-3.1-8B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false
resize_token_embeddings_to_32x: false
flash_attention: true
xformers_attention:
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: skymizer/Llama3.1-8B-base-tokenized-fineweb-edu-45B-4096
train_on_split: train
type: completion
test_datasets:
- path: skymizer/Llama3.1-8B-base-tokenized-fineweb-edu-test-4K
split: test
type: completion
is_preprocess: true
skip_prepare_dataset: true
dataset_prepared_path: /mnt/home/model-team/datasets/pretokenized/Llama3.1-8B-base-tokenized-fineweb-edu-45B-4096
hf_use_auth_token: true
output_dir: /mnt/home/model-team/models/Llama3.1-8B-v0.1-relu-stage-1-fineweb-edu-45B-4096
resume_from_checkpoint:
auto_resume_from_checkpoints: true
sequence_len: 4096
sample_packing: true
sample_packing_group_size: 100000
sample_packing_bin_size: 200
pad_to_sequence_len: true
eval_sample_packing: false
# eval_causal_lm_metrics: ["perplexity"]
wandb_project: "sparse-tuning-cpt"
wandb_entity:
wandb_watch:
wandb_name: "Llama3.1-8B-relu-stage-1-fineweb-edu-45B-4096"
wandb_log_model:
# global batch size = 2 * 8 * 8 GPUs * 8 Nodes * 4096 = 4M
gradient_accumulation_steps: 8
micro_batch_size: 2
# eval_batch_size: 2
max_steps: 10000
optimizer: adamw_torch
learning_rate: 0.000015
lr_scheduler: cosine
cosine_min_lr_ratio: 1.0
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.95
adam_eps: 0.000001
max_grad_norm: 1.0
train_on_inputs: false
group_by_length: false
bf16: true
fp16:
tf32: false
hub_model_id: "skymizer/Llama3.1-8B-relu-stage-1-fineweb-edu-45B-4096"
save_strategy: "steps"
save_steps: 500
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
warmup_steps: 1
eval_steps: 500
eval_table_size:
debug:
deepspeed: /root/train/axolotl/deepspeed_configs/zero3_bf16.json
fsdp:
fsdp_config:
seed: 42
special_tokens:
pad_token: "<|end_of_text|>"
Llama3.1-8B-relu-stage-1-fineweb-edu-45B-4096
This model is a fine-tuned version of meta-llama/Llama-3.1-8B on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.9682
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: 1.5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 64
- gradient_accumulation_steps: 8
- total_train_batch_size: 1024
- total_eval_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 2
- training_steps: 10000
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
12.2232 | 0.0001 | 1 | 12.1487 |
2.2025 | 0.0424 | 500 | 2.2272 |
2.1454 | 0.0848 | 1000 | 2.1515 |
2.0991 | 0.1273 | 1500 | 2.1142 |
2.0604 | 0.1697 | 2000 | 2.0894 |
2.058 | 0.2121 | 2500 | 2.0711 |
2.0582 | 0.2545 | 3000 | 2.0561 |
2.0474 | 0.2969 | 3500 | 2.0442 |
2.0268 | 0.3394 | 4000 | 2.0347 |
2.0173 | 0.3818 | 4500 | 2.0256 |
1.9941 | 0.4242 | 5000 | 2.0178 |
2.0113 | 0.4666 | 5500 | 2.0106 |
1.9949 | 0.5091 | 6000 | 2.0040 |
2.0077 | 0.5515 | 6500 | 1.9984 |
1.986 | 0.5939 | 7000 | 1.9935 |
1.9902 | 0.6363 | 7500 | 1.9888 |
1.9899 | 0.6787 | 8000 | 1.9841 |
1.9729 | 0.7212 | 8500 | 1.9800 |
1.971 | 0.7636 | 9000 | 1.9759 |
1.9784 | 0.8060 | 9500 | 1.9718 |
1.9553 | 0.8484 | 10000 | 1.9682 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
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