See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: sethuiyer/Medichat-Llama3-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - d16d145cf1763c87_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/d16d145cf1763c87_train_data.json
  type:
    field_input: text
    field_instruction: context
    field_output: label
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device: cuda
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config:
  max_steps: 50
  weight_decay: 0.01
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: nadejdatarabukina/927707f3-54e5-4156-b71b-a043cf7f5121
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: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 70GiB
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/d16d145cf1763c87_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
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: 70
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 927707f3-54e5-4156-b71b-a043cf7f5121
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 927707f3-54e5-4156-b71b-a043cf7f5121
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

927707f3-54e5-4156-b71b-a043cf7f5121

This model is a fine-tuned version of sethuiyer/Medichat-Llama3-8B on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0859

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_TORCH 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: 50

Training results

Training Loss	Epoch	Step	Validation Loss
11.7715	0.0007	1	12.6951
12.6567	0.0034	5	11.2645
2.3684	0.0068	10	0.7841
0.7793	0.0103	15	0.1361
0.0084	0.0137	20	0.2027
0.0001	0.0171	25	0.1864
0.0149	0.0205	30	0.2243
0.0015	0.0240	35	0.1061
0.0021	0.0274	40	0.0988
0.003	0.0308	45	0.0874
0.2611	0.0342	50	0.0859

Framework versions

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