See axolotl config
axolotl version: 0.4.1
base_model: codellama/CodeLlama-34b-Python-hf
model_type: LlamaForCausalLM
tokenizer_type: CodeLlamaTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: afrias5/FinUpTagsNoTestNoExNew
type: alpaca
field: text
dataset_prepared_path: ./FinUpTagsNoTestNoExNew
val_set_size: 0
output_dir: models/codellamaTest1
lora_model_dir: models/codellamaTest/checkpoint-60
auto_resume_from_checkpoints: true
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: False
adapter: lora
lora_model_dir:
lora_r: 2
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_modules_to_save:
- embed_tokens
- lm_head
wandb_project: 'Code32bTDualGPU'
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_name: 'code34bResumeNewFI2TestNew10' #change
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 8
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: true
fp16:
tf32: false
hub_model_id: afrias5/codellama-cont2
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: false
s2_attention:
logging_steps: 1
warmup_steps: 10
# eval_steps: 300
saves_per_epoch: 1
save_total_limit: 12
debug:
deepspeed:
weight_decay: 0.0
fsdp:
deepspeed: deepspeed_configs/zero3_bf16.json
fsdp_config:
special_tokens:
bos_token: "<s>"
eos_token: "</s>"
unk_token: "<unk>"
codellama-cont2
This model is a fine-tuned version of codellama/CodeLlama-34b-Python-hf on the None dataset.
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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 8
Training results
Framework versions
- PEFT 0.11.1
- Transformers 4.42.4
- Pytorch 2.2.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
- Downloads last month
- 3
Model tree for afrias5/codellama-cont2
Base model
codellama/CodeLlama-34b-Python-hf