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nttx/
8550c988-ecb5-4702-a4dc-1b399381592f

Name: 8550c988-ecb5-4702-a4dc-1b399381592f
Availability: InStock

generated_from_trainer

unsloth/llama-3-8b

adapter

llama3

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: unsloth/llama-3-8b
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
  - 2a9666cd49da86d0_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/2a9666cd49da86d0_train_data.json
  type:
    field_instruction: formatted_question
    field_output: combinedfact
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 2
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 150
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: nttx/8550c988-ecb5-4702-a4dc-1b399381592f
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
  0: 75GB
max_steps: 1200
micro_batch_size: 4
mlflow_experiment_name: /tmp/2a9666cd49da86d0_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
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: 150
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: ebeabb86-0fb6-4027-a194-b22f71fcb6f6
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: ebeabb86-0fb6-4027-a194-b22f71fcb6f6
warmup_steps: 50
weight_decay: 0.0
xformers_attention: null

8550c988-ecb5-4702-a4dc-1b399381592f

This model is a fine-tuned version of unsloth/llama-3-8b on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6277

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
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: 50
training_steps: 1200

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0019	1	2.7032
0.6383	0.2808	150	0.7222
0.6734	0.5615	300	0.6478
0.6087	0.8423	450	0.6217
0.369	1.1231	600	0.6357
0.4278	1.4038	750	0.6277

Framework versions

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