You can deploy this model and start earning money today!

apriasmoro/
44b52ca0-74e5-443f-9888-3199a705ea89

Name: 44b52ca0-74e5-443f-9888-3199a705ea89
Availability: InStock

generated_from_trainer

unsloth/Llama-3.2-1B-Instruct

adapter

llama3.2

Views

No views yet

See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: unsloth/Llama-3.2-1B-Instruct
bf16: true
chat_template: llama3
datasets:
- data_files:
  - 13b25555ef00608b_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: 256
evals_per_epoch: 2
flash_attention: false
fp16: false
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: true
hub_model_id: apriasmoro/44b52ca0-74e5-443f-9888-3199a705ea89
learning_rate: 0.0002
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: 40
micro_batch_size: 8
mlflow_experiment_name: /tmp/13b25555ef00608b_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
sample_packing: false
save_steps: 5
sequence_len: 2048
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: f43ba9f4-cc47-44a3-a3f7-ed45bc401d8c
wandb_project: Gradients-On-Demand
wandb_run: apriasmoro
wandb_runid: f43ba9f4-cc47-44a3-a3f7-ed45bc401d8c
warmup_steps: 100
weight_decay: 0.01

44b52ca0-74e5-443f-9888-3199a705ea89

This model is a fine-tuned version of unsloth/Llama-3.2-1B-Instruct 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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_BNB 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: 40

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0006	1	nan
No log	0.0040	7	nan
0.0	0.0080	14	nan
0.0	0.0119	21	nan
0.0	0.0159	28	nan
0.0	0.0199	35	nan

Framework versions

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