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nttx/
2c5a80b9-ec59-41d0-b50e-f70b11c17ff7

Name: 2c5a80b9-ec59-41d0-b50e-f70b11c17ff7
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: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
  - 19532d4afbbe1c70_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/19532d4afbbe1c70_train_data.json
  type:
    field_input: input
    field_instruction: instruction
    field_output: output
    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: 8
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: 2
gradient_checkpointing: true
group_by_length: true
hub_model_id: nttx/2c5a80b9-ec59-41d0-b50e-f70b11c17ff7
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 3e-5
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
  0: 75GB
max_steps: 1500
micro_batch_size: 8
mlflow_experiment_name: /tmp/19532d4afbbe1c70_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 15
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.999
  adam_epsilon: 1e-8
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: false
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 150
saves_per_epoch: null
sequence_len: 1024
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: 8032b002-362c-4442-a806-99808b885d3f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8032b002-362c-4442-a806-99808b885d3f
warmup_steps: 50
weight_decay: 0.1
xformers_attention: null

2c5a80b9-ec59-41d0-b50e-f70b11c17ff7

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: 1.2183

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: 3e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
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=0.999,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	0.0004	1	1.7817
1.3639	0.0528	150	1.3274
1.3636	0.1057	300	1.2837
1.2255	0.1585	450	1.2648
1.2236	0.2113	600	1.2463
1.3322	0.2641	750	1.2392
1.1567	0.3170	900	1.2308
1.2132	0.3698	1050	1.2241
1.2745	0.4226	1200	1.2197
1.1855	0.4754	1350	1.2175
1.2677	0.5283	1500	1.2183

Framework versions

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