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Llama-3-8B-Magpie-Pro-MT-SFT-v0.1

Text Generation
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PyTorch
Safetensors
llama
axolotl
Generated from Trainer
conversational
text-generation-inference
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Llama-3-8B-Magpie-Pro-MT-SFT-v0.1 / README.md
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metadata
license: llama3
base_model: meta-llama/Meta-Llama-3-8B
tags:
  - axolotl
  - generated_from_trainer
model-index:
  - name: Llama-3-8B-SynDa-70BQA-300K-Filtered-MR-L
    results: []

Built with Axolotl

See axolotl config

axolotl version: 0.4.0


base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: SynDa/Llama-3-70B-SynDa-MultiRound-300K-Filtered-L
    type: sharegpt
    conversation: llama3
dataset_prepared_path: last_run_prepared
val_set_size: 0.001
output_dir: ./out_Llama-3-70B-SynDa-300K-Multi-Round2-L

sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

wandb_project: SynDa
wandb_entity:
wandb_watch:
wandb_name: Llama-3-70B-SynDa-300K-MR-L-2EP-FFT
wandb_log_model:
hub_model_id: SynDa/Llama-3-8B-SynDa-70BQA-300K-Filtered-MR-L

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5

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

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
evals_per_epoch: 3
eval_table_size:
saves_per_epoch: 3
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>

Llama-3-8B-SynDa-70BQA-300K-Filtered-MR-L

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4555

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: 4
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 32
  • total_eval_batch_size: 4
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 2

Training results

Training Loss Epoch Step Validation Loss
0.8807 0.0007 1 0.9001
0.5113 0.3337 464 0.5178
0.4668 0.6673 928 0.4792
0.4492 1.0010 1392 0.4582
0.3498 1.3205 1856 0.4575
0.3525 1.6542 2320 0.4555

Framework versions

  • Transformers 4.40.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1