thewimo/Mistral-7B-v0.3-deide-lora-adapter-v0.1

See axolotl config

axolotl version: 0.4.0

base_model: mistralai/Mistral-7B-v0.3
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: true
load_in_4bit: false
strict: false

datasets:
  - path: thewimo/german-medical-identification-dataset-v0.1
    type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.2
output_dir: ./outputs/lora-out
hub_model_id: thewimo/Mistral-7B-v0.3-deide-phi

adapter: lora
lora_model_dir:

sequence_len: 4096
sample_packing: false
pad_to_sequence_len: true

lora_r: 8
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: axolotl-runs
wandb_entity: thewind-mom-finetuning 
wandb_watch:
wandb_name: Mistral-7B-v0.3-deide-phi
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 4
optimizer: paged_adamw_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-v0.3-deide-phi

This model is a fine-tuned version of mistralai/Mistral-7B-v0.3 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0364

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: 4
• eval_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 3
• gradient_accumulation_steps: 4
• total_train_batch_size: 48
• total_eval_batch_size: 12
• 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
1.9682	0.0506	1	2.0579
1.2784	0.2532	5	0.8308
0.187	0.5063	10	0.1732
0.1094	0.7595	15	0.0819
0.0542	1.0127	20	0.0593
0.0354	1.2658	25	0.0521
0.0493	1.5190	30	0.0457
0.038	1.7722	35	0.0432
0.0143	2.0253	40	0.0425
0.0269	2.2785	45	0.0423
0.0273	2.5316	50	0.0415
0.0277	2.7848	55	0.0366
0.0288	3.0380	60	0.0356
0.0241	3.2911	65	0.0358
0.0125	3.5443	70	0.0362
0.0164	3.7975	75	0.0364

Framework versions

• PEFT 0.10.0
• Transformers 4.40.2
• Pytorch 2.1.2+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1