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

lora_fan_in_fan_out: false
data_seed: 49
seed: 49

datasets:
  - path: sample_data/alpaca_synth_queries.jsonl
    type: sharegpt
    conversation: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./qlora-alpaca-out
hub_model_id: caldana/hc-mistral-alpaca

adapter: qlora
lora_model_dir:

sequence_len: 896
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: 

gradient_accumulation_steps: 4
micro_batch_size: 16
eval_batch_size: 16
num_epochs: 100
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
max_grad_norm: 1.0
adam_beta2: 0.95
adam_epsilon: 0.00001
save_total_limit: 12

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: 20
evals_per_epoch: 3
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 6
debug:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"
save_safetensors: true

hc-mistral-alpaca

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: 0.3648

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: 16
eval_batch_size: 16
seed: 49
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 20
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss
1.334	0.6667	1	1.2849
1.3476	1.3333	2	1.2780
1.2981	2.0	3	1.2487
1.3157	2.6667	4	1.1840
1.1757	3.3333	5	1.0690
1.1376	4.0	6	0.9086
0.9395	4.6667	7	0.7184
0.7385	5.3333	8	0.5617
0.5541	6.0	9	0.4307
0.4056	6.6667	10	0.3257
0.2791	7.3333	11	0.2866
0.2198	8.0	12	0.2453
0.1746	8.6667	13	0.2167
0.1582	9.3333	14	0.2104
0.1515	10.0	15	0.1699
0.1168	10.6667	16	0.1502
0.087	11.3333	17	0.1415
0.1	12.0	18	0.1574
0.0832	12.6667	19	0.1699
0.0765	13.3333	20	0.1601
0.0697	14.0	21	0.1544
0.0625	14.6667	22	0.1653
0.0583	15.3333	23	0.1628
0.047	16.0	24	0.1463
0.0366	16.6667	25	0.1637
0.0342	17.3333	26	0.2020
0.0398	18.0	27	0.1801
0.0319	18.6667	28	0.1835
0.0229	19.3333	29	0.1957
0.0286	20.0	30	0.2024
0.0166	20.6667	31	0.2519
0.0184	21.3333	32	0.2699
0.0129	22.0	33	0.2813
0.0109	22.6667	34	0.2950
0.0105	23.3333	35	0.3037
0.0111	24.0	36	0.3161
0.0071	24.6667	37	0.3310
0.0115	25.3333	38	0.3375
0.0051	26.0	39	0.3456
0.004	26.6667	40	0.3488
0.0077	27.3333	41	0.3599
0.0028	28.0	42	0.3706
0.0021	28.6667	43	0.3737
0.002	29.3333	44	0.3729
0.0017	30.0	45	0.3742
0.0013	30.6667	46	0.3757
0.0004	31.3333	47	0.3755
0.0006	32.0	48	0.3764
0.0002	32.6667	49	0.3750
0.0011	33.3333	50	0.3646
0.0005	34.0	51	0.3586
0.0013	34.6667	52	0.3617
0.0005	35.3333	53	0.3638
0.0011	36.0	54	0.3657
0.0003	36.6667	55	0.3710
0.0002	37.3333	56	0.3711
0.0004	38.0	57	0.3736
0.0003	38.6667	58	0.3784
0.0001	39.3333	59	0.3795
0.0007	40.0	60	0.3737
0.0001	40.6667	61	0.3730
0.0003	41.3333	62	0.3729
0.0002	42.0	63	0.3714
0.0001	42.6667	64	0.3698
0.0001	43.3333	65	0.3704
0.0001	44.0	66	0.3704
0.0001	44.6667	67	0.3705
0.0001	45.3333	68	0.3655
0.0002	46.0	69	0.3672
0.0002	46.6667	70	0.3682
0.0002	47.3333	71	0.3656
0.0001	48.0	72	0.3663
0.0001	48.6667	73	0.3668
0.0001	49.3333	74	0.3673
0.0001	50.0	75	0.3638
0.0001	50.6667	76	0.3640
0.0001	51.3333	77	0.3643
0.0001	52.0	78	0.3640
0.0001	52.6667	79	0.3648
0.0001	53.3333	80	0.3629
0.0001	54.0	81	0.3648
0.0001	54.6667	82	0.3617
0.0001	55.3333	83	0.3632
0.0001	56.0	84	0.3650
0.0001	56.6667	85	0.3636
0.0001	57.3333	86	0.3633
0.0001	58.0	87	0.3673
0.0001	58.6667	88	0.3663
0.0001	59.3333	89	0.3618
0.0001	60.0	90	0.3635
0.0001	60.6667	91	0.3605
0.0001	61.3333	92	0.3654
0.0001	62.0	93	0.3647
0.0001	62.6667	94	0.3586
0.0001	63.3333	95	0.3601
0.0001	64.0	96	0.3631
0.0001	64.6667	97	0.3629
0.0001	65.3333	98	0.3652
0.0001	66.0	99	0.3645
0.0001	66.6667	100	0.3648

Framework versions

PEFT 0.10.0
Transformers 4.40.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1