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abaddon182/
b22401f8-4ac4-4c65-9a9c-b72ce0e6ab4c

Name: b22401f8-4ac4-4c65-9a9c-b72ce0e6ab4c
Availability: InStock

generated_from_trainer

unsloth/Meta-Llama-3.1-8B

adapter

llama3.1

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: unsloth/Meta-Llama-3.1-8B
bf16: true
chat_template: llama3
dataloader_num_workers: 24
dataset_prepared_path: null
datasets:
- data_files:
  - c8863bd2f47db118_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/c8863bd2f47db118_train_data.json
  type:
    field_input: Script
    field_instruction: Language
    field_output: Content
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 3
eval_batch_size: 2
eval_max_new_tokens: 128
eval_steps: 300
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 16
gradient_checkpointing: false
group_by_length: true
hub_model_id: abaddon182/b22401f8-4ac4-4c65-9a9c-b72ce0e6ab4c
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5e-5
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
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_grad_norm: 1.0
max_memory:
  0: 75GB
max_steps: 1500
micro_batch_size: 2
mlflow_experiment_name: /tmp/c8863bd2f47db118_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.999
  adam_epsilon: 1e-8
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: 300
saves_per_epoch: null
sequence_len: 512
strict: false
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: 54a9ab7f-3855-47b6-a6ed-d8494c906f6f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 54a9ab7f-3855-47b6-a6ed-d8494c906f6f
warmup_steps: 50
weight_decay: 0.1
xformers_attention: null

b22401f8-4ac4-4c65-9a9c-b72ce0e6ab4c

This model is a fine-tuned version of unsloth/Meta-Llama-3.1-8B on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.2892

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: 5e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.999,adam_epsilon=1e-8
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 50
training_steps: 1500

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0015	1	2.2589
1.3138	0.4438	300	1.4589
1.2793	0.8877	600	1.3675
1.3665	1.3317	900	1.3156
1.3232	1.7755	1200	1.2935
1.0842	2.2195	1500	1.2892

Framework versions

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