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lu-vae/llama-8b-fft

sourceHugging Faceupdated 2y agoView on Hugging Face
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Model Card

<!-- 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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary>

axolotl version: 0.4.0

yaml
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: /data/data/final_set_cleaned/train/
    type: sharegpt
    conversation: chatml
  - path: /data/data/map_coig_cqia.jsonl
    type: sharegpt
    conversation: chatml
  - path: /data/data/ruozhiba.jsonl
    type: sharegpt
    conversation: chatml
dataset_prepared_path: last_run_prepared
val_set_size: 0
output_dir: ./out

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

wandb_project: FFT
wandb_entity:
wandb_watch:
wandb_name: llama3-fft
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 4
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: 0
eval_table_size:
saves_per_epoch: 4
save_total_limit: 8
debug:
deepspeed: deepspeed/zero2.json
weight_decay: 0.0
fsdp:
fsdp_config:
default_system_message: "You are a helpful assistant."
special_tokens:
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
tokens:
  - "<|im_start|>"
  - "<|im_end|>"

</details><br>

llama-8B-fft

This model was trained from scratch 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: 2e-05
  • —trainbatchsize: 4
  • —evalbatchsize: 4
  • —seed: 42
  • —distributed_type: multi-GPU
  • —num_devices: 6
  • —gradientaccumulationsteps: 8
  • —totaltrainbatch_size: 192
  • —totalevalbatch_size: 24
  • —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • —lrschedulertype: cosine
  • —lrschedulerwarmup_steps: 100
  • —num_epochs: 2

Training results

Framework versions

  • —Transformers 4.40.1
  • —Pytorch 2.0.1+cu118
  • —Datasets 2.15.0
  • —Tokenizers 0.19.1