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library_name: transformers
license: llama3.1
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- generated_from_trainer
datasets:
- tokenized_novelty_dataset_4/train_full.parquet
model-index:
- name: outputs_4/llama31-8B-liger-ds-full
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. -->
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<details><summary>See axolotl config</summary>
axolotl version: `0.12.2`
```yaml
base_model: meta-llama/Llama-3.1-8B-Instruct
#load_in_4bit: true
#adapter: lora
#lora_r: 16
#lora_alpha: 32
#lora_dropout: 0.05
#lora_target_modules:
# - q_proj
# - v_proj
# - k_proj
# - o_proj
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: true
strict: false
chat_template: llama3
datasets:
- path: "tokenized_novelty_dataset_4/train_full.parquet"
type:
ds_type: parquet
dataset_prepared_path:
val_set_size: 0.00
output_dir: ./outputs_4/llama31-8B-liger-ds-full
dataset_processes: 16
sequence_len: 32120
sample_packing: false
pad_to_sequence_len: true
gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 3
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 2e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true
warmup_steps: 50
evals_per_epoch: 0
eval_table_size:
saves_per_epoch: 2
save_only_model: true
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: <|finetune_right_pad_id|>
eos_token: <|eot_id|>
```
</details><br>
# outputs_4/llama31-8B-liger-ds-full
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the tokenized_novelty_dataset_4/train_full.parquet dataset.
## 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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 32
- total_train_batch_size: 32
- total_eval_batch_size: 32
- 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: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 2433
### Training results
### Framework versions
- Transformers 4.55.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
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