base_model: mistralai/Mistral-7B-Instruct-v0.2
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: data.jsonl
    ds_type: json
    type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./mistral-out

adapter: qlora
lora_model_dir:

hub_model_id: Burhan02/Mistral-7B-Instruct-v0.2-ft
hub_strategy: every_save
hf_use_auth_token: true

sequence_len: 8192
sample_packing: true
eval_sample_packing: false
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:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 18
optimizer: adamw_bnb_8bit
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

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

Mistral-7B-Instruct-v0.2-ft

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1573

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: 18

Training results

Training Loss	Epoch	Step	Validation Loss
0.2077	1.0	1	0.3832
0.1985	2.0	2	0.3782
0.1979	2.0	3	0.3553
0.1901	3.0	4	0.3147
0.1624	3.0	5	0.2756
0.1489	4.0	6	0.2525
0.1342	4.0	7	0.2383
0.1137	5.0	8	0.2228
0.1026	5.0	9	0.2040
0.1001	6.0	10	0.1905
0.0828	6.0	11	0.1816
0.0746	7.0	12	0.1751
0.0687	7.0	13	0.1707
0.0544	8.0	14	0.1654
0.0526	8.0	15	0.1620
0.0469	9.0	16	0.1591
0.048	9.0	17	0.1575
0.0392	10.0	18	0.1573

Framework versions

PEFT 0.10.0
Transformers 4.40.0.dev0
Pytorch 2.1.2+cu121
Datasets 2.15.0
Tokenizers 0.15.0