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dimasik1987/
7f0c6181-b5ed-44c8-8052-5b6d4a03f1e0

Name: 7f0c6181-b5ed-44c8-8052-5b6d4a03f1e0
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: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 78da8ad1845a67f6_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/78da8ad1845a67f6_train_data.json
  type:
    field_input: context
    field_instruction: query
    field_output: option_0
    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_steps: 25
eval_table_size: null
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: true
hub_model_id: dimasik1987/7f0c6181-b5ed-44c8-8052-5b6d4a03f1e0
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: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 74GiB
max_steps: 75
micro_batch_size: 2
mlflow_experiment_name: /tmp/78da8ad1845a67f6_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1e-5
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: 2048
special_tokens:
  pad_token: <|end_of_text|>
strict: false
tf32: null
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 7f0c6181-b5ed-44c8-8052-5b6d4a03f1e0
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 7f0c6181-b5ed-44c8-8052-5b6d4a03f1e0
warmup_ratio: 0.05
weight_decay: 0.01
xformers_attention: true

7f0c6181-b5ed-44c8-8052-5b6d4a03f1e0

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: 1.9671

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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 3
training_steps: 75

Training results

Training Loss	Epoch	Step	Validation Loss
2.8783	0.0008	1	2.6536
1.8783	0.0208	25	2.0331
3.212	0.0415	50	1.9783
1.7919	0.0623	75	1.9671

Framework versions

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