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dada22231/
6e2e8ac1-9179-4cba-864a-500efdf87ac8

Name: 6e2e8ac1-9179-4cba-864a-500efdf87ac8
Availability: InStock

generated_from_trainer

custom_code

NousResearch/CodeLlama-7b-hf

adapter

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: NousResearch/CodeLlama-7b-hf
bf16: auto
chat_template: llama3
cosine_min_lr_ratio: 0.1
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
  - 8cfa16af45c0f632_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/8cfa16af45c0f632_train_data.json
  type:
    field_input: context
    field_instruction: question
    field_output: answer
    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: dada22231/6e2e8ac1-9179-4cba-864a-500efdf87ac8
hub_strategy: checkpoint
hub_token: null
hub_username: dada22231
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: 200
micro_batch_size: 1
mlflow_experiment_name: /tmp/8cfa16af45c0f632_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
repository_id: dada22231/6e2e8ac1-9179-4cba-864a-500efdf87ac8
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 25
save_strategy: steps
sequence_len: 2048
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: 6e2e8ac1-9179-4cba-864a-500efdf87ac8
wandb_project: Public_TuningSN
wandb_runid: 6e2e8ac1-9179-4cba-864a-500efdf87ac8
warmup_ratio: 0.04
weight_decay: 0.01
xformers_attention: null

6e2e8ac1-9179-4cba-864a-500efdf87ac8

This model is a fine-tuned version of NousResearch/CodeLlama-7b-hf on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3835

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: 8
training_steps: 200

Training results

Training Loss	Epoch	Step	Validation Loss
14.5112	0.0026	1	0.6907
20.5603	0.0648	25	0.4967
20.1954	0.1297	50	0.4543
14.8141	0.1945	75	0.4304
15.3476	0.2594	100	0.4095
12.4383	0.3242	125	0.3978
15.0081	0.3890	150	0.3895
13.6059	0.4539	175	0.3860
13.933	0.5187	200	0.3835

Framework versions

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