llama3-8b-pippa

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<details><summary>See axolotl config</summary>

axolotl version: 0.4.0

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

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
#  - path: taozi555/bagel
#    type: sharegpt
  - path: MinervaAI/Aesir-Preview
    type: sharegpt
  - path: KaraKaraWitch/PIPPA-ShareGPT-formatted
    type: sharegpt
chat_template: chatml

dataset_prepared_path: last_run_prepared
val_set_size: 0.001
output_dir: /workspace/llama3-8b-pippa
adapter: qlora
lora_model_dir:

sequence_len: 8192
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:
lora_modules_to_save:
  - embed_tokens
  - lm_head

wandb_project: waifu
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
adam_beta2: 0.95
adam_epsilon: 0.00001
max_grad_norm: 1.0
lr_scheduler: cosine
learning_rate: 0.0002
optimizer: paged_adamw_32bit

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
#bfloat16: true

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

warmup_steps: 10

eval_steps: 100
eval_table_size:
eval_table_max_new_tokens:
eval_sample_packing: false
saves_per_epoch: 
save_steps: 100
save_total_limit: 2
debug:
#deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16_cpuoffload_all.json
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
  eos_token: "<|im_end|>"
  pad_token: "<|im_end|>"
tokens:
  - "<|im_start|>"

</details><br>

workspace/llama3-8b-pippa

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

• Loss: 1.5946

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: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 10
• num_epochs: 4

Training results

Framework versions

• PEFT 0.10.0
• Transformers 4.40.0.dev0
• Pytorch 2.2.0+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0