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cimol/
6c9a5d77-e617-4dfa-9203-c0cf4ef0a5ab

generated_from_trainer

TinyLlama/TinyLlama-1.1B-Chat-v1.0

adapter

apache-2.0

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
bf16: true
chat_template: llama3
data_processes: 24
dataset_prepared_path: null
datasets:
- data_files:
  - 01a2f31ee699a7d7_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/01a2f31ee699a7d7_train_data.json
  type:
    field_input: meshid
    field_instruction: meshMajor
    field_output: abstractText
    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: 2
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 150
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: true
hub_model_id: cimol/6c9a5d77-e617-4dfa-9203-c0cf4ef0a5ab
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 50
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: constant
lr_scheduler_warmup_steps: 50
max_grad_norm: 1.0
max_memory:
  0: 75GB
max_steps: 8972
micro_batch_size: 4
mlflow_experiment_name: /tmp/01a2f31ee699a7d7_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
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: 150
saves_per_epoch: null
seed: 17333
sequence_len: 512
strict: false
tf32: true
tokenizer_type: AutoTokenizer
total_train_batch_size: 16
train_batch_size: 4
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: eb72191c-e324-465a-ad03-de3b4175193b
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: eb72191c-e324-465a-ad03-de3b4175193b
warmup_steps: 50
weight_decay: 0.0
xformers_attention: null

6c9a5d77-e617-4dfa-9203-c0cf4ef0a5ab

This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.8554

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: 17333
gradient_accumulation_steps: 4
total_train_batch_size: 16
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: constant
lr_scheduler_warmup_steps: 50
training_steps: 8972

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0003	1	1.9516
1.8616	0.0507	150	1.8699
1.8641	0.1015	300	1.8637
1.8496	0.1522	450	1.8614
1.851	0.2029	600	1.8592
1.8411	0.2536	750	1.8571
1.8402	0.3044	900	1.8552
1.8516	0.3551	1050	1.8564
1.8275	0.4058	1200	1.8543
1.8455	0.4565	1350	1.8592
1.8356	0.5073	1500	1.8554

Framework versions

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