See axolotl config
axolotl version: 0.4.1
adapter: lora
base_model: katuni4ka/tiny-random-qwen1.5-moe
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 4af3f7c1fa5610ea_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4af3f7c1fa5610ea_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 500
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso06/5663e6cc-de8d-4ea7-87d4-be498fce4d0e
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000206
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 50
lora_alpha: 128
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 9000
micro_batch_size: 4
mlflow_experiment_name: /tmp/4af3f7c1fa5610ea_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 500
saves_per_epoch: null
seed: 60
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 1798553a-3812-4302-9a96-9f20992e85cb
wandb_project: 06a
wandb_run: your_name
wandb_runid: 1798553a-3812-4302-9a96-9f20992e85cb
warmup_steps: 100
weight_decay: 0.0
xformers_attention: null
5663e6cc-de8d-4ea7-87d4-be498fce4d0e
This model is a fine-tuned version of katuni4ka/tiny-random-qwen1.5-moe on the None dataset. It achieves the following results on the evaluation set:
- Loss: 11.7402
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: 0.000206
- train_batch_size: 4
- eval_batch_size: 4
- seed: 60
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100
- training_steps: 9000
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
No log | 0.0003 | 1 | 11.9364 |
11.7934 | 0.1516 | 500 | 11.7890 |
11.7818 | 0.3033 | 1000 | 11.7749 |
11.7769 | 0.4549 | 1500 | 11.7676 |
11.772 | 0.6066 | 2000 | 11.7620 |
11.7681 | 0.7582 | 2500 | 11.7579 |
11.76 | 0.9098 | 3000 | 11.7542 |
11.7532 | 1.0615 | 3500 | 11.7502 |
11.774 | 1.2131 | 4000 | 11.7484 |
11.7583 | 1.3648 | 4500 | 11.7457 |
11.7423 | 1.5164 | 5000 | 11.7446 |
11.7536 | 1.6681 | 5500 | 11.7429 |
11.7444 | 1.8197 | 6000 | 11.7420 |
11.753 | 1.9713 | 6500 | 11.7410 |
11.7538 | 2.1230 | 7000 | 11.7406 |
11.7483 | 2.2746 | 7500 | 11.7404 |
11.7539 | 2.4263 | 8000 | 11.7401 |
11.7568 | 2.5779 | 8500 | 11.7402 |
11.7279 | 2.7295 | 9000 | 11.7402 |
Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
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Model tree for lesso06/5663e6cc-de8d-4ea7-87d4-be498fce4d0e
Base model
katuni4ka/tiny-random-qwen1.5-moe