YoungPanda/qwenqwen

See axolotl config

axolotl version: 0.4.0

base_model: Qwen/Qwen1.5-MoE-A2.7B
trust_remote_code: true

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: Drewskidang/chatlaw
    type: sharegpt
  - path: swag/articles_and_summaries.jsonl
    ds_type: json # see other options below
    type: summarizetldr

dataset_prepared_path:
val_set_size: 0.05
output_dir: ./out

sequence_len: 4096  # supports up to 32k
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false

wandb_project: Qwen Qwen
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

out

This model is a fine-tuned version of Qwen/Qwen1.5-MoE-A2.7B on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.8947

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
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 8
• total_train_batch_size: 256
• total_eval_batch_size: 32
• 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
1.6446	0.13	1	1.6456
1.639	0.26	2	1.3070
1.1786	0.52	4	1.1381
1.0398	0.79	6	1.0396
1.0073	1.02	8	1.0162
0.9318	1.28	10	1.0095
0.9704	1.54	12	0.9867
0.8477	1.8	14	0.9405
0.7665	2.03	16	0.9073
0.6283	2.3	18	0.9021
0.6257	2.56	20	0.8947

Framework versions

• Transformers 4.40.0.dev0
• Pytorch 2.1.1
• Datasets 2.18.0
• Tokenizers 0.15.0