skymizer/Llama3.1-8B-relu-stage-2-dolma-v1_7-50B-4096
06
<!-- 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"/> <details><summary>See axolotl config</summary>
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-base-tokenized-dolma-v1_7-50B
train_on_split: train
type: completion
test_datasets:
- path: skymizer/Llama3.1-tokenized-dolma-v1_7-test
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-dolma-v1_7_50B-4096
hf_use_auth_token: true
output_dir: /mnt/home/model-team/models/Llama3.1-8B-relu-stage-2-dolma-50B-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-dolma-50B-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
num_epochs: 1
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-2-dolma-v1_7-50B-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|>"
</details><br>
Llama3.1-8B-relu-stage-2-dolma-v1_7-50B-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: 2.4342
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
- trainbatchsize: 2
- evalbatchsize: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 64
- gradientaccumulationsteps: 8
- totaltrainbatch_size: 1024
- totalevalbatch_size: 128
- optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.95) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
- lrschedulertype: cosine
- lrschedulerwarmup_steps: 2
- num_epochs: 1
Training results
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3