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Alphatao/
1a85a43c-4102-4d60-81a4-0a94c87e7143

Name: 1a85a43c-4102-4d60-81a4-0a94c87e7143
Availability: InStock

generated_from_trainer

VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct

adapter

llama3.1

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - ca76119e6c5fd025_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/ca76119e6c5fd025_train_data.json
  type:
    field_input: plan
    field_instruction: goal
    field_output: revision
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
device_map:
  ? ''
  : 0,1,2,3,4,5,6,7
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 400
eval_table_size: null
flash_attention: true
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: Alphatao/1a85a43c-4102-4d60-81a4-0a94c87e7143
hub_repo: null
hub_strategy: null
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- down_proj
- up_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 4434
micro_batch_size: 2
mlflow_experiment_name: /tmp/ca76119e6c5fd025_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
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: 400
sequence_len: 2048
special_tokens:
  pad_token: <|eot_id|>
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: 96a743b2-a958-4214-9d5b-c5b688f4e563
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 96a743b2-a958-4214-9d5b-c5b688f4e563
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

1a85a43c-4102-4d60-81a4-0a94c87e7143

This model is a fine-tuned version of VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.0634

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: 4
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
training_steps: 4434

Training results

Training Loss	Epoch	Step	Validation Loss
2.0656	0.0001	1	1.9550
1.1439	0.0558	400	1.1639
0.8577	0.1115	800	1.1436
1.1815	0.1673	1200	1.1263
0.955	0.2230	1600	1.1152
1.1593	0.2788	2000	1.1032
0.8844	0.3345	2400	1.0939
1.0242	0.3903	2800	1.0816
0.9839	0.4460	3200	1.0735
1.1426	0.5018	3600	1.0675
0.7592	0.5575	4000	1.0641
0.8164	0.6133	4400	1.0634

Framework versions

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