See axolotl config

axolotl version: 0.8.0

base_model: meta-llama/Llama-3.1-8B-Instruct

load_in_8bit: false
load_in_4bit: false
strict: false

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: Sandevistan_cleaned.jsonl
    type: customllama3_stan
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./outputs/out

fix_untrained_tokens: true

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

wandb_project: Pneuma
wandb_entity: 
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 16
micro_batch_size: 8
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.000075
max_grad_norm: 1

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: unsloth
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
eval_sample_packing: false

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true

hub_model_id: Replete-AI/L3-Pneuma-8B
hub_strategy: every_save

warmup_steps: 10
evals_per_epoch: 3
eval_table_size:
saves_per_epoch: 3
debug:
deepspeed:
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|begin_of_text|>"
  eos_token: "<|end_of_text|>"
  pad_token: "<|end_of_text|>"
tokens:

L3-Pneuma-8B

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on the Sandevistan_cleaned.jsonl dataset. It achieves the following results on the evaluation set:

• Loss: 0.7796

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: 7.5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 16
• total_train_batch_size: 128
• optimizer: Use OptimizerNames.PAGED_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: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss
1.3399	0.0023	1	1.3175
0.846	0.3332	143	0.8312
0.8103	0.6665	286	0.8021
0.7617	0.9997	429	0.7737
0.5824	1.3309	572	0.7851
0.5651	1.6641	715	0.7798
0.5738	1.9974	858	0.7796

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.5.0
• Tokenizers 0.21.1