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bane5631/
6a62cc9d-cfd9-45f1-a7c6-e49ea437edc8

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:
  - e89f96913218f8de_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/e89f96913218f8de_train_data.json
  type:
    field_input: intent
    field_instruction: instruction
    field_output: response
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: bane5631/6a62cc9d-cfd9-45f1-a7c6-e49ea437edc8
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
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_memory:
  0: 75GB
max_steps: 400
micro_batch_size: 8
mlflow_experiment_name: /tmp/e89f96913218f8de_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1e-5
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: 50
saves_per_epoch: null
sequence_len: 1024
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: a5f829a5-85bb-442f-a490-e8bbfde3b08d
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: a5f829a5-85bb-442f-a490-e8bbfde3b08d
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

6a62cc9d-cfd9-45f1-a7c6-e49ea437edc8

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: 10.2674

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: 8
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
training_steps: 400

Training results

Training Loss	Epoch	Step	Validation Loss
10.3743	0.0009	1	10.3783
10.3449	0.0434	50	10.3441
10.3163	0.0868	100	10.3182
10.2906	0.1302	150	10.2944
10.279	0.1736	200	10.2812
10.2687	0.2170	250	10.2733
10.268	0.2604	300	10.2692
10.2661	0.3038	350	10.2677
10.2617	0.3472	400	10.2674

Framework versions

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