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Alphatao/
47df94be-b459-4f7e-b986-4e35d785e9c6

Name: 47df94be-b459-4f7e-b986-4e35d785e9c6
Availability: InStock

generated_from_trainer

NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer

adapter

other

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - d466cf0a618a6d44_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/d466cf0a618a6d44_train_data.json
  type:
    field_input: context
    field_instruction: question
    field_output: answer
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
device_map:
  ? ''
  : 0,1,2,3,4,5,6,7
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: false
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: Alphatao/47df94be-b459-4f7e-b986-4e35d785e9c6
hub_repo: null
hub_strategy: null
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.3
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 966
micro_batch_size: 4
mlflow_experiment_name: /tmp/d466cf0a618a6d44_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
sequence_len: 2048
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.011452812123488802
wandb_entity: null
wandb_mode: online
wandb_name: 9c8b08cf-d8fc-4fe1-9436-20a6f16a593b
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 9c8b08cf-d8fc-4fe1-9436-20a6f16a593b
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

47df94be-b459-4f7e-b986-4e35d785e9c6

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

Loss: 1.4092

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
training_steps: 966

Training results

Training Loss	Epoch	Step	Validation Loss
3.2163	0.0001	1	3.4837
1.4579	0.0074	100	1.5107
1.3924	0.0148	200	1.5177
1.4023	0.0222	300	1.4957
1.419	0.0297	400	1.4808
1.2275	0.0371	500	1.4610
1.4388	0.0445	600	1.4429
1.3089	0.0519	700	1.4245
1.7182	0.0593	800	1.4133
1.6928	0.0667	900	1.4092

Framework versions

PEFT 0.13.2
Transformers 4.46.0
Pytorch 2.5.0+cu124
Datasets 3.0.1
Tokenizers 0.20.1