See axolotl config
axolotl version: 0.4.1
adapter: lora
base_model: fxmarty/tiny-random-GemmaForCausalLM
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 5063485566247712_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/5063485566247712_train_data.json
type:
field_input: context
field_instruction: prompt
field_output: answer
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 3
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: dimasik2987/47167887-b6d8-46b7-ad4d-ce2468acae61
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: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: linear
max_memory:
0: 70GiB
max_steps: 25
micro_batch_size: 4
mlflow_experiment_name: /tmp/5063485566247712_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: 25
save_strategy: steps
sequence_len: 4056
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: 47167887-b6d8-46b7-ad4d-ce2468acae61
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 47167887-b6d8-46b7-ad4d-ce2468acae61
warmup_ratio: 0.05
weight_decay: 0.01
xformers_attention: null
47167887-b6d8-46b7-ad4d-ce2468acae61
This model is a fine-tuned version of fxmarty/tiny-random-GemmaForCausalLM 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 25
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
0.0 | 0.0003 | 1 | nan |
0.0 | 0.0008 | 3 | nan |
0.0 | 0.0017 | 6 | nan |
0.0 | 0.0025 | 9 | nan |
0.0 | 0.0033 | 12 | nan |
0.0 | 0.0041 | 15 | nan |
0.0 | 0.0050 | 18 | nan |
0.0 | 0.0058 | 21 | nan |
0.0 | 0.0066 | 24 | nan |
Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
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Model tree for dimasik2987/47167887-b6d8-46b7-ad4d-ce2468acae61
Base model
fxmarty/tiny-random-GemmaForCausalLM