minionai/llama_3.1_70b_cove_prod_81424_amazon_filt_merged

See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: meta-llama/Meta-Llama-3.1-70B
bf16: true
dataset_prepared_path: last_run_prepared
debug: null
deepspeed: null
early_stopping_patience: null
eval_table_size: null
evals_per_epoch: 0
flash_attention: true
fp16: null
deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
group_by_length: false
hub_model_id: minionai/llama_3.1_70b_cove_prod_81424_amazon_filt
hub_strategy: all_checkpoints
learning_rate: 1e-4
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lora_target_modules: null
lr_scheduler: cosine
micro_batch_size: 1
model_type: LlamaForCausalLM
num_epochs: 3
optimizer: adamw_torch
output_dir: ./lora-out
pad_to_sequence_len: true
resume_from_checkpoint: null
sample_packing: true
wandb_entity: minionai
wandb_name: llama_3.1_70b_cove_prod_81424_amazon_filt
wandb_project: llama3.1-70b
saves_per_epoch: 1
sequence_len: 8192
special_tokens:
  pad_token: <|end_of_text|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
val_set_size: 0
warmup_steps: 150
weight_decay: 0.0
datasets:
- path: minionai/cove_prod_81424_amazon_filt_ift
  type: 
      system_prompt: ""
      system_format: "{system}"
      field_system: system
      field_instruction: instruction
      field_input: input
      field_output: output
      format: |-
        User: {instruction} {input}
        Assistant:
      # 'no_input_format' cannot include {input}
      no_input_format: "### System:\nBelow is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\nverify(\""

llama_3.1_70b_cove_prod_81424_amazon_filt

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-70B 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: 0.0001
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• total_train_batch_size: 8
• total_eval_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 150
• num_epochs: 3

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