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JoshMe1/
695d8154-ac35-446e-8b80-949750b3ef78

Name: 695d8154-ac35-446e-8b80-949750b3ef78
Availability: InStock

generated_from_trainer

NousResearch/Llama-3.2-1B

adapter

llama3.2

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: NousResearch/Llama-3.2-1B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - ffd2a98c07b7ffeb_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: ''
debug: null
deepspeed: null
device_map: auto
dynamic_lora_per_layer: true
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
evaluation_strategy: steps
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: false
hub_model_id: JoshMe1/695d8154-ac35-446e-8b80-949750b3ef78
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: 10
lora_alpha: 256
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 128
lora_target_linear: true
lr_finder: true
lr_scheduler: cosine
lr_scheduler_args: []
max_memory:
  0: 130GB
max_steps: 350
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/ffd2a98c07b7ffeb_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
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: 100
save_strategy: steps
save_total_limit: 3
saves_per_epoch: null
scheduler:
  factor: 0.5
  monitor: eval_loss
  patience: 1
  threshold: 0.01
  type: ReduceLROnPlateau
sequence_len: 2048
special_tokens:
  pad_token: <|end_of_text|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
training_stages:
- learning_rate: 0.0002
  name: warmup
  num_train_epochs: 1
- learning_rate: 2.0e-05
  name: main
trl:
  ema: true
  ema_decay: 0.999
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: a34533cb-a7c4-45ad-b411-dfe62917b9a0
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: a34533cb-a7c4-45ad-b411-dfe62917b9a0
warmup_steps: 35
weight_decay: 0.01
xformers_attention: true

695d8154-ac35-446e-8b80-949750b3ef78

This model is a fine-tuned version of NousResearch/Llama-3.2-1B on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.9516

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: 35
training_steps: 350

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0001	1	2.2459
1.0669	0.0148	100	1.1048
1.1959	0.0295	200	1.0143
1.0919	0.0443	300	0.9516

Framework versions

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