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eeeebbb2/
48df83ef-ccda-4d30-91be-c59f2d6bc6d5

Name: 48df83ef-ccda-4d30-91be-c59f2d6bc6d5
Availability: InStock

generated_from_trainer

TinyLlama/TinyLlama_v1.1

adapter

apache-2.0

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: TinyLlama/TinyLlama_v1.1
bf16: auto
chat_template: llama3
cosine_min_lr_ratio: 0.1
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
  - 2a1ce2f60d87890e_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/2a1ce2f60d87890e_train_data.json
  type:
    field_input: docstring_tokens
    field_instruction: doc
    field_output: code
    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: 1
eval_batch_size: 1
eval_sample_packing: false
eval_steps: 25
evaluation_strategy: steps
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 32
gradient_checkpointing: true
group_by_length: true
hub_model_id: eeeebbb2/48df83ef-ccda-4d30-91be-c59f2d6bc6d5
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
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
- v_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
  0: 70GiB
  1: 70GiB
  2: 70GiB
  3: 70GiB
max_steps: 150
micro_batch_size: 1
mlflow_experiment_name: /tmp/2a1ce2f60d87890e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1e-5
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: 25
save_strategy: steps
sequence_len: 2048
special_tokens:
  pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
torch_compile: false
train_on_inputs: false
trust_remote_code: true
val_set_size: 50
wandb_entity: null
wandb_mode: online
wandb_name: 48df83ef-ccda-4d30-91be-c59f2d6bc6d5
wandb_project: Public_TuningSN
wandb_runid: 48df83ef-ccda-4d30-91be-c59f2d6bc6d5
warmup_ratio: 0.04
weight_decay: 0.01
xformers_attention: null

48df83ef-ccda-4d30-91be-c59f2d6bc6d5

This model is a fine-tuned version of TinyLlama/TinyLlama_v1.1 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.8292

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: 0.0001
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 32
total_train_batch_size: 128
total_eval_batch_size: 4
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-5
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 6
training_steps: 150

Training results

Training Loss	Epoch	Step	Validation Loss
1.8656	0.0064	1	2.4903
1.1077	0.1604	25	1.0209
0.9852	0.3207	50	0.9231
0.9338	0.4811	75	0.8805
0.8435	0.6414	100	0.8542
0.8634	0.8018	125	0.8382
0.8225	0.9621	150	0.8292

Framework versions

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