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sniperfix/
aa2b30f1-d004-4383-89d7-b99e2b328598

Name: aa2b30f1-d004-4383-89d7-b99e2b328598
Availability: InStock

generated_from_trainer

NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer

adapter

other

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 976789cb5685b201_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/976789cb5685b201_train_data.json
  type:
    field_instruction: name
    field_output: notes
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 256
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 32
gradient_checkpointing: true
group_by_length: false
hub_model_id: sniperfix/aa2b30f1-d004-4383-89d7-b99e2b328598
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: 3
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- gate_proj
- down_proj
- up_proj
lr_scheduler: cosine
max_grad_norm: 2
max_steps: 90
micro_batch_size: 2
mlflow_experiment_name: /tmp/976789cb5685b201_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1.0e-05
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: indexjupri-sniper-country
wandb_mode: online
wandb_name: 9f693ec1-00c4-4c4b-a9a1-1c6d5153d95a
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 9f693ec1-00c4-4c4b-a9a1-1c6d5153d95a
warmup_steps: 20
weight_decay: 0.02
xformers_attention: false

aa2b30f1-d004-4383-89d7-b99e2b328598

This model is a fine-tuned version of NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.3705

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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 32
total_train_batch_size: 64
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-05
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 20
training_steps: 90

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0020	1	2.8712
2.7839	0.0163	8	2.6537
2.4408	0.0327	16	2.4638
2.4066	0.0490	24	2.4257
2.3882	0.0654	32	2.4094
2.3834	0.0817	40	2.3966
2.3703	0.0980	48	2.3908
2.3024	0.1144	56	2.3859
2.4025	0.1307	64	2.3792
2.3677	0.1471	72	2.3730
2.3133	0.1634	80	2.3718
2.3879	0.1798	88	2.3705

Framework versions

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