Instructions to use bitzic/god-d573960c-cal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use bitzic/god-d573960c-cal with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("oopsung/llama2-7b-koNqa-test-v1") model = PeftModel.from_pretrained(base_model, "bitzic/god-d573960c-cal") - Notebooks
- Google Colab
- Kaggle
See axolotl config
axolotl version: 0.11.0.dev0
adapter: lora
base_model: oopsung/llama2-7b-koNqa-test-v1
bf16: auto
chat_template: llama3
dataloader_num_workers: 1
dataloader_pin_memory: false
dataset_prepared_path: null
datasets:
- data_files:
- d573960c-4c46-4d22-9469-55c8c20ddf00_train_data.json
ds_type: json
format: custom
path: /workspace/axolotl/data
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_batch_size: 2
eval_max_new_tokens: 128
eval_sample_packing: false
eval_steps: 41
eval_table_size: null
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
greater_is_better: false
group_by_length: false
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_steps: 82
metric_for_best_model: eval_loss
micro_batch_size: 8
mlflow_experiment_name: /workspace/axolotl/data/d573960c-4c46-4d22-9469-55c8c20ddf00_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: /app/checkpoints/d573960c-4c46-4d22-9469-55c8c20ddf00/god-d573960c-cal
pad_to_sequence_len: false
resume_from_checkpoint: null
s2_attention: null
sample_packing: true
save_steps: 41
save_total_limit: 3
sdp_attention: true
sequence_len: 4096
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.005633802816901409
wandb_entity: null
wandb_mode: offline
wandb_name: d573960c-4c46-4d22-9469-55c8c20ddf00_god-d573960c-cal
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: d573960c-4c46-4d22-9469-55c8c20ddf00_god-d573960c-cal
warmup_ratio: 0.03
weight_decay: 0.01
xformers_attention: null
app/checkpoints/d573960c-4c46-4d22-9469-55c8c20ddf00/god-d573960c-cal
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.3357
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: 8
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 2
- training_steps: 82
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0 | 0 | 1.8332 |
| 2.2122 | 0.0203 | 41 | 1.4023 |
| 1.715 | 0.0406 | 82 | 1.3357 |
Framework versions
- PEFT 0.15.2
- Transformers 4.53.1
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.4
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Model tree for bitzic/god-d573960c-cal
Base model
oopsung/llama2-7b-koNqa-test-v1