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tryingpro/
cc1ab05c-e5ee-4432-a796-9b7e7053e2d1

Name: cc1ab05c-e5ee-4432-a796-9b7e7053e2d1
Availability: InStock

generated_from_trainer

NousResearch/Meta-Llama-3-8B

adapter

other

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: NousResearch/Meta-Llama-3-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - da92ec7138cf7572_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/da92ec7138cf7572_train_data.json
  type:
    field_instruction: ctx
    field_output: chosen
    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_steps: 25
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 32
gradient_checkpointing: true
group_by_length: false
hub_model_id: tryingpro/cc1ab05c-e5ee-4432-a796-9b7e7053e2d1
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: 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
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: 125
micro_batch_size: 2
mlflow_experiment_name: /tmp/da92ec7138cf7572_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
special_tokens:
  pad_token: <|end_of_text|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: tryingpro-unicourt
wandb_mode: online
wandb_name: 67e38e0f-12cd-4211-887d-8de51deebf53
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 67e38e0f-12cd-4211-887d-8de51deebf53
warmup_steps: 20
weight_decay: 0.02
xformers_attention: false

cc1ab05c-e5ee-4432-a796-9b7e7053e2d1

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

Loss: 1.3898

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: 125

Training results

Training Loss	Epoch	Step	Validation Loss
1.7211	0.0007	1	1.6361
1.3775	0.0178	25	1.4690
1.4813	0.0356	50	1.4351
1.3868	0.0534	75	1.4108
1.3393	0.0712	100	1.3943
1.344	0.0890	125	1.3898

Framework versions

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