Total runs:
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kloodia / lora-8b-math
Model's Last Updated: April 29 2024
Introduction of lora-8b-math
Model Details of lora-8b-math
See axolotl config
axolotl version:
0.4.0
base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: true
load_in_4bit: false
strict: false
datasets:
- path: kloodia/raw_math
type: oasst
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./lora-out-math
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
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: 4
micro_batch_size: 1
num_epochs: 3
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
s2_attention:
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:
pad_token: <|end_of_text|>
lora-out-math
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0946
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
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 4
- 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 |
|---|---|---|---|
| 1.1956 | 0.0 | 1 | 1.1464 |
| 1.1544 | 0.25 | 180 | 1.0905 |
| 1.1932 | 0.5 | 360 | 1.0890 |
| 1.1827 | 0.75 | 540 | 1.0873 |
| 1.0816 | 1.0 | 720 | 1.0861 |
| 1.0741 | 1.24 | 900 | 1.0887 |
| 1.0849 | 1.49 | 1080 | 1.0885 |
| 1.0629 | 1.74 | 1260 | 1.0878 |
| 1.0165 | 1.99 | 1440 | 1.0866 |
| 1.1012 | 2.22 | 1620 | 1.0938 |
| 1.0574 | 2.47 | 1800 | 1.0943 |
| 1.033 | 2.72 | 1980 | 1.0946 |
Framework versions
- PEFT 0.10.0
- Transformers 4.40.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
Runs of kloodia lora-8b-math on huggingface.co
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24-hour runs
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3-day runs
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30-day runs
More Information About lora-8b-math huggingface.co Model
Url of lora-8b-math
lora-8b-math huggingface.co Url
Provider of lora-8b-math huggingface.co
Other API from kloodia
Updated:October 12 2026
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