---
library_name: transformers
license: llama3.1
base_model: meta-llama/Llama-3.1-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: Llama3.1-8B-v0.1-dolma-skymizer-method-0.6
  results: []
---

<!-- 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"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.5.2`
```yaml
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-v0.1-STE-0.6
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-v0.1-dolma-STE-0.6"
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: 1
max_steps: 10000
optimizer: adamw_torch
learning_rate: 0.00005
lr_scheduler: cosine
cosine_min_lr_ratio: 0.2
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-v0.1-dolma-skymizer-method-0.6"

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: 375
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-v0.1-dolma-skymizer-method-0.6

This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3883

## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 64
- gradient_accumulation_steps: 8
- total_train_batch_size: 1024
- total_eval_batch_size: 64
- 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: 375
- training_steps: 10000

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 2.2837        | 0.0001 | 1     | 2.5425          |
| 2.2557        | 0.0414 | 500   | 2.4568          |
| 2.2641        | 0.0829 | 1000  | 2.4520          |
| 2.2207        | 0.1243 | 1500  | 2.4477          |
| 2.3003        | 0.1657 | 2000  | 2.4432          |
| 2.2382        | 0.2072 | 2500  | 2.4388          |
| 2.2339        | 0.2486 | 3000  | 2.4349          |
| 2.2517        | 0.2901 | 3500  | 2.4303          |
| 2.2483        | 0.3315 | 4000  | 2.4246          |
| 2.2067        | 0.3729 | 4500  | 2.4207          |
| 2.2485        | 0.4144 | 5000  | 2.4163          |
| 2.2541        | 0.4558 | 5500  | 2.4123          |
| 2.2192        | 0.4972 | 6000  | 2.4084          |
| 2.2346        | 0.5387 | 6500  | 2.4041          |
| 2.2106        | 0.5801 | 7000  | 2.4010          |
| 2.2112        | 0.6215 | 7500  | 2.3982          |
| 2.2215        | 0.6630 | 8000  | 2.3951          |
| 2.2118        | 0.7044 | 8500  | 2.3924          |
| 2.1933        | 0.7458 | 9000  | 2.3905          |
| 2.1813        | 0.7873 | 9500  | 2.3893          |
| 2.1969        | 0.8287 | 10000 | 2.3883          |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3