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axolotl_llama3.1_trial

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llama
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metadata
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
library_name: peft
license: llama3.1
tags:
  - axolotl
  - generated_from_trainer
model-index:
  - name: axolotl_llama3.1_trial
    results: []

Built with Axolotl

See axolotl config

axolotl version: 0.4.1

base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: pavan01729/GPT_AI_5.0_alpaca
    type: alpaca
dataset_prepared_path:
val_set_size: 0
output_dir: ./outputs/llama3_qlora_out

adapter: qlora
lora_model_dir:

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 1
# num_epochs: 100
max_steps: 10
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002

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

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: "<|end_of_text|>"


# mlflow configuration
mlflow_tracking_uri: "http://127.0.0.1:5000"  # Replace with your MLflow server URI
mlflow_experiment_name: "llama3.1_8b"
hf_mlflow_log_artifacts: true  # Set to true to log checkpoints as MLflow artifacts


hub_model_id: "pavan01729/axolotl_llama3.1_trial"  # Set your Hugging Face repository ID here
push_to_hub: true  # Set to true to enable pushing the model to the HF Hub

axolotl_llama3.1_trial

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on the None 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: 0.0002
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 10
  • training_steps: 10

Training results

Framework versions

  • PEFT 0.12.0
  • Transformers 4.44.0
  • Pytorch 2.3.1+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1