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ivangrapher/
fda384c7-6ed2-4d5b-9ce8-691a8cdf3853

Name: fda384c7-6ed2-4d5b-9ce8-691a8cdf3853
Availability: InStock

generated_from_trainer

huggyllama/llama-7b

adapter

other

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: huggyllama/llama-7b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 8886f510771c72d5_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/8886f510771c72d5_train_data.json
  type:
    field_instruction: "\uC81C\uBAA9"
    field_output: "\uBCF8\uBB38"
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device: cuda
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config:
  max_steps: 50
  weight_decay: 0.01
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: ivangrapher/fda384c7-6ed2-4d5b-9ce8-691a8cdf3853
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 70GiB
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/8886f510771c72d5_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 70
sequence_len: 2048
special_tokens:
  pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: fda384c7-6ed2-4d5b-9ce8-691a8cdf3853
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: fda384c7-6ed2-4d5b-9ce8-691a8cdf3853
warmup_steps: 10
weight_decay: 0.0
xformers_attention: true

fda384c7-6ed2-4d5b-9ce8-691a8cdf3853

This model is a fine-tuned version of huggyllama/llama-7b on the None dataset. It achieves the following results on the evaluation set:

Loss: nan

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
gradient_accumulation_steps: 4
total_train_batch_size: 8
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: 10
training_steps: 50

Training results

Epoch	Step	Validation Loss
0.0001	1	nan
0.0004	5	nan
0.0008	10	nan
0.0013	15	nan
0.0017	20	nan
0.0021	25	nan
0.0025	30	nan
0.0030	35	nan
0.0034	40	nan
0.0038	45	nan
0.0042	50	nan

Framework versions

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