See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: 01-ai/Yi-1.5-9B-Chat-16K
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - c475efe990de9bf2_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/c475efe990de9bf2_train_data.json
  type:
    field_instruction: instruction
    field_output: output
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: oldiday/2afa9331-21dc-47d6-8db1-187461b968e2
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: 10
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
  0: 75GB
max_steps: 600
micro_batch_size: 8
mlflow_experiment_name: /tmp/c475efe990de9bf2_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1.0e-05
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: 50
saves_per_epoch: null
sequence_len: 512
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: techspear-hub
wandb_mode: online
wandb_name: ad597699-1989-4271-8eac-a2b3d418b84a
wandb_project: Gradients-On-Six
wandb_run: your_name
wandb_runid: ad597699-1989-4271-8eac-a2b3d418b84a
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

2afa9331-21dc-47d6-8db1-187461b968e2

This model is a fine-tuned version of 01-ai/Yi-1.5-9B-Chat-16K on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.0801

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: 8
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Use OptimizerNames.ADAMW_BNB 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: 10
• training_steps: 600

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0007	1	1.8997
1.6253	0.0327	50	1.3680
1.533	0.0653	100	1.3633
1.5809	0.0980	150	1.2979
1.503	0.1307	200	1.2213
1.5156	0.1633	250	1.1558
1.3948	0.1960	300	1.1323
1.5253	0.2286	350	1.1079
1.431	0.2613	400	1.0921
1.501	0.2940	450	1.0856
1.5452	0.3266	500	1.0834
1.4285	0.3593	550	1.0820
1.572	0.3920	600	1.0801

Framework versions

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