See axolotl config
axolotl version: 0.4.1
base_model: meta-llama/Meta-Llama-3-8B-Instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: /workspace/axolotl/mathvi/input_output_meta_llama_3_8b_instruct-00000-of-00001.parquet
type: input_output
dataset_prepared_path:
val_set_size: 0.05
eval_sample_packing: false
output_dir: mathvi/output_model2
sequence_len: 4096
sample_packing: false
pad_to_sequence_len: false
adapter: lora
lora_model_dir:
lora_r: 64
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 32
micro_batch_size: 4
num_epochs: 3
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 2e-4
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
s2_attention:
loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3
warmup_steps: 10
evals_per_epoch: 10
eval_table_size:
eval_max_new_tokens: 512
saves_per_epoch: 2
save_total_limit: 20
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: <|end_of_text|>
mathvi/output_model2
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3327
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
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- 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 |
---|---|---|---|
2.0442 | 0.0190 | 1 | 2.0734 |
1.449 | 0.1137 | 6 | 1.2774 |
0.8548 | 0.2275 | 12 | 0.9006 |
0.8561 | 0.3412 | 18 | 0.7924 |
0.744 | 0.4550 | 24 | 0.7176 |
0.6752 | 0.5687 | 30 | 0.6603 |
0.5908 | 0.6825 | 36 | 0.6117 |
0.5229 | 0.7962 | 42 | 0.5702 |
0.558 | 0.9100 | 48 | 0.5281 |
0.4343 | 1.0237 | 54 | 0.4752 |
0.4039 | 1.1374 | 60 | 0.4152 |
0.3744 | 1.2512 | 66 | 0.4225 |
0.3313 | 1.3649 | 72 | 0.3852 |
0.374 | 1.4787 | 78 | 0.3740 |
0.3246 | 1.5924 | 84 | 0.3657 |
0.3392 | 1.7062 | 90 | 0.3591 |
0.3309 | 1.8199 | 96 | 0.3505 |
0.3621 | 1.9336 | 102 | 0.3437 |
0.2819 | 2.0474 | 108 | 0.3416 |
0.2672 | 2.1611 | 114 | 0.3414 |
0.2284 | 2.2749 | 120 | 0.3375 |
0.2836 | 2.3886 | 126 | 0.3353 |
0.2504 | 2.5024 | 132 | 0.3337 |
0.2696 | 2.6161 | 138 | 0.3328 |
0.2775 | 2.7299 | 144 | 0.3327 |
0.2554 | 2.8436 | 150 | 0.3325 |
0.2551 | 2.9573 | 156 | 0.3327 |
Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.1.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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