base_model: mistralai/Mistral-7B-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true

load_in_8bit: false
load_in_4bit: true
strict: false

hub_model_id: nitsw/mistral_axonotll
datasets:
  - path: nitsw/alpaca_cleaned
    type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./qlora-out

adapter: qlora
lora_model_dir:

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj

wandb_project: swapnil_axolotl
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

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

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

warmup_steps: 10
# evals_per_epoch: 4
eval_steps: 10
eval_table_size:
eval_table_max_new_tokens: 128
# saves_per_epoch: 1
save_steps: 10
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

mistral_axonotll

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.8484

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
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
0.8523	0.06	10	0.8987
0.8882	0.13	20	0.8766
0.8374	0.19	30	0.8683
0.8223	0.25	40	0.8636
0.85	0.32	50	0.8604
0.8425	0.38	60	0.8577
0.8572	0.44	70	0.8560
0.8427	0.51	80	0.8539
0.8627	0.57	90	0.8526
0.8242	0.63	100	0.8512
0.8555	0.7	110	0.8501
0.8348	0.76	120	0.8495
0.8593	0.83	130	0.8488
0.8403	0.89	140	0.8485
0.8628	0.95	150	0.8484

Framework versions

PEFT 0.7.2.dev0
Transformers 4.38.0.dev0
Pytorch 2.0.1
Datasets 2.16.1
Tokenizers 0.15.0