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segopecelus/
4c8eedd9-ca98-4bee-a9dc-2044805fc0c2

Name: 4c8eedd9-ca98-4bee-a9dc-2044805fc0c2
Availability: InStock

generated_from_trainer

TinyLlama/TinyLlama_v1.1

adapter

apache-2.0

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See axolotl config

axolotl version: 0.10.0.dev0

yaml

adapter: lora
base_model: TinyLlama/TinyLlama_v1.1
bf16: true
datasets:
- data_files:
  - 55f5bcb41b160895_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/
  type:
    field_instruction: instruct
    field_output: output
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
eval_max_new_tokens: 128
evals_per_epoch: 4
flash_attention: false
fp16: false
gradient_accumulation_steps: 1
gradient_checkpointing: true
group_by_length: true
hub_model_id: segopecelus/4c8eedd9-ca98-4bee-a9dc-2044805fc0c2
learning_rate: 0.0002
load_in_4bit: false
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: false
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 2226
micro_batch_size: 60
mlflow_experiment_name: /tmp/55f5bcb41b160895_train_data.json
output_dir: miner_id_24
rl: null
sample_packing: true
save_steps: 333
sequence_len: 2048
special_tokens:
  pad_token: </s>
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: true
trl: null
trust_remote_code: true
wandb_name: dbe382da-84d4-4363-9c63-24934368605f
wandb_project: Gradients-On-Demand
wandb_run: apriasmoro
wandb_runid: dbe382da-84d4-4363-9c63-24934368605f
warmup_steps: 100
weight_decay: 0.01

4c8eedd9-ca98-4bee-a9dc-2044805fc0c2

This model is a fine-tuned version of TinyLlama/TinyLlama_v1.1 on an unknown 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: 0.0002
train_batch_size: 60
eval_batch_size: 60
seed: 42
distributed_type: multi-GPU
num_devices: 2
total_train_batch_size: 120
total_eval_batch_size: 120
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100
training_steps: 2226

Training results

Framework versions

PEFT 0.15.2
Transformers 4.51.3
Pytorch 2.5.1+cu124
Datasets 3.5.1
Tokenizers 0.21.1