Deadwalker0/maverick-34b-qlora

See axolotl config

axolotl version: 0.4.0

base_model: codellama/CodeLlama-34b-hf
model_type: LlamaForCausalLM
tokenizer_type: CodeLlamaTokenizer
is_llama_derived_model: true

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: iamtarun/code_instructions_120k_alpaca
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./maverick34b

adapter: qlora
lora_model_dir:

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

maverick34b

This model is a fine-tuned version of codellama/CodeLlama-34b-hf on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.3391

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
• distributed_type: multi-GPU
• num_devices: 7
• gradient_accumulation_steps: 4
• total_train_batch_size: 56
• total_eval_batch_size: 14
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 10
• num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss
0.5065	0.01	1	0.5089
0.3477	0.25	29	0.3561
0.3593	0.51	58	0.3461
0.3329	0.76	87	0.3423
0.3607	1.0	116	0.3404
0.3336	1.26	145	0.3395
0.3449	1.51	174	0.3386
0.3187	1.77	203	0.3377
0.3216	2.0	232	0.3371
0.2961	2.26	261	0.3380
0.3117	2.51	290	0.3381
0.3207	2.77	319	0.3379
0.3047	3.01	348	0.3376
0.3096	3.26	377	0.3391
0.3148	3.52	406	0.3391
0.3116	3.77	435	0.3391

Framework versions

• PEFT 0.8.2
• Transformers 4.38.0.dev0
• Pytorch 2.0.1+cu118
• Datasets 2.17.0
• Tokenizers 0.15.0