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fats-fme/
2282480b-ef3b-4136-8e90-4dd3c99bfab5

Name: 2282480b-ef3b-4136-8e90-4dd3c99bfab5
Availability: InStock

generated_from_trainer

fxmarty/tiny-llama-fast-tokenizer

adapter

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: fxmarty/tiny-llama-fast-tokenizer
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 3b22e525b2bdcb03_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/3b22e525b2bdcb03_train_data.json
  type:
    field_input: tools
    field_instruction: query
    field_output: answers
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
ddp_find_unused_parameters: false
distributed_type: ddp
early_stopping_patience: null
env:
  CUDA_VISIBLE_DEVICES: 0,1
  MASTER_ADDR: localhost
  MASTER_PORT: '29500'
  NCCL_DEBUG: INFO
  NCCL_IB_DISABLE: '0'
  NCCL_P2P_DISABLE: '0'
  NCCL_P2P_LEVEL: NVL
  PYTORCH_CUDA_ALLOC_CONF: max_split_size_mb:512, garbage_collection_threshold:0.8
  WORLD_SIZE: '2'
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: true
fp16: false
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: true
hub_model_id: fats-fme/2282480b-ef3b-4136-8e90-4dd3c99bfab5
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_memory_MB: 65000
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/3b22e525b2bdcb03_train_data.json
model_type: AutoModelForCausalLM
num_devices: 2
num_epochs: 1
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 4056
special_tokens:
  pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 2282480b-ef3b-4136-8e90-4dd3c99bfab5
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 2282480b-ef3b-4136-8e90-4dd3c99bfab5
warmup_steps: 20
world_size: 2
xformers_attention: true

2282480b-ef3b-4136-8e90-4dd3c99bfab5

This model is a fine-tuned version of fxmarty/tiny-llama-fast-tokenizer on the None dataset. It achieves the following results on the evaluation set:

Loss: 10.3855

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
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 4
total_train_batch_size: 16
total_eval_batch_size: 4
optimizer: Use OptimizerNames.ADAMW_TORCH 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: 20
training_steps: 10

Training results

Training Loss	Epoch	Step	Validation Loss
10.3873	0.0003	1	10.3866
10.3872	0.0009	3	10.3866
10.3879	0.0017	6	10.3861
10.3829	0.0026	9	10.3855

Framework versions

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