aidonuts/fused-v0

See axolotl config

axolotl version: 0.6.0

base_model: Qwen/Qwen2.5-Coder-7B-Instruct

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  # geopandas
  - path: https://www.fused.io/server/v1/realtime-shared/fsh_7UePa8c68x8u89FjmK2Tuu/run/file?dtype_out_vector=parquet
    type: pretrain
    ds_type: parquet
    text_column: text
    split: train
  # examples
  - path: https://staging.fused.io/server/v1/realtime-shared/fsh_2xCVySNfnwmUhWPssX24cn/run/file?dtype_out_raster=png&dtype_out_vector=parquet&cb=12345
    type: pretrain
    ds_type: parquet
    text_column: text
    split: train
  # docs
  - path: https://www.fused.io/server/v1/realtime-shared/fsh_EycsvX70Y3WosxHhdJ8Y9/run/file?dtype_out_raster=png&dtype_out_vector=parquet
    type: pretrain
    ds_type: parquet
    text_column: text
    split: train
  - path: mlabonne/FineTome-100k
    type: chat_template
    split: train[:1%]
    chat_template: qwen_25
    field_messages: conversations
    message_field_role: from
    message_field_content: value

dataset_prepared_path: last_run_prepared
val_set_size: 0.
output_dir: ./outputs/qlora-out

wandb_project: fused-io-copilot
wandb_entity: axolotl-ai
wandb_watch:
wandb_name:
wandb_log_model:

sequence_len: 8192
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: false


gradient_accumulation_steps: 2
micro_batch_size: 4
num_epochs: 2
optimizer: lion_8bit
lr_scheduler: cosine
learning_rate: 0.00001

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

gradient_checkpointing: true
logging_steps: 1
flash_attention: true

warmup_steps: 20
saves_per_epoch: 1
deepspeed:
weight_decay: 0.01
special_tokens:
  pad_token: "<|end_of_text|>"

save_safetensors: true

outputs/qlora-out

This model is a fine-tuned version of Qwen/Qwen2.5-Coder-7B-Instruct on the https://www.fused.io/server/v1/realtime-shared/fsh_7UePa8c68x8u89FjmK2Tuu/run/file?dtype_out_vector=parquet, the https://staging.fused.io/server/v1/realtime-shared/fsh_2xCVySNfnwmUhWPssX24cn/run/file?dtype_out_raster=png&dtype_out_vector=parquet&cb=12345, the https://www.fused.io/server/v1/realtime-shared/fsh_EycsvX70Y3WosxHhdJ8Y9/run/file?dtype_out_raster=png&dtype_out_vector=parquet and the mlabonne/FineTome-100k datasets.

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: 1e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 8
• optimizer: Use OptimizerNames.LION_8BIT and the args are: No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 20
• num_epochs: 2

Training results

Framework versions

• Transformers 4.47.0
• Pytorch 2.5.1+cu124
• Datasets 3.1.0
• Tokenizers 0.21.0