bobyres/mistral_label

See axolotl config

axolotl version: 0.4.0

base_model: bofenghuang/vigogne-2-7b-instruct
base_model_config: bofenghuang/vigogne-2-7b-instruct
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: bobyres/LabelV01
    type: alpaca 

dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./qlora-out
adapter: qlora
lora_model_dir:

sequence_len: 4096
sample_packing: false
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: "finetune_labelisation_v011"
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model: "checkpoint"

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 3
optimizer: paged_adamw_32bit
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

warmup_steps: 10
eval_steps: 0.05
eval_table_size:
save_steps:
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

qlora-out

This model is a fine-tuned version of bofenghuang/vigogne-2-7b-instruct on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.6592

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: 1
• eval_batch_size: 1
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 10
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
1.0134	0.03	1	1.0981
0.972	0.15	6	1.0736
0.7982	0.31	12	0.8548
0.6944	0.46	18	0.7151
0.6808	0.62	24	0.6943
0.6763	0.77	30	0.6821
0.67	0.92	36	0.6764
0.6424	1.08	42	0.6730
0.6552	1.23	48	0.6780
0.6527	1.38	54	0.6690
0.6624	1.54	60	0.6632
0.6228	1.69	66	0.6625
0.6447	1.85	72	0.6617
0.6409	2.0	78	0.6599
0.6356	2.15	84	0.6589
0.648	2.31	90	0.6584
0.6254	2.46	96	0.6593
0.6167	2.62	102	0.6596
0.6451	2.77	108	0.6590
0.6144	2.92	114	0.6592

Framework versions

• PEFT 0.11.0
• Transformers 4.39.0.dev0
• Pytorch 2.0.1+cu118
• Datasets 2.17.1
• Tokenizers 0.15.0