library_name, license, base_model, tags, datasets, model-index

library_name	license	base_model	tags	datasets	model-index
transformers	apache-2.0	Qwen/Qwen2.5-Coder-7B-Instruct	axolotl generated_from_trainer	train.jsonl	name results wizl_base_7b-fsv

See axolotl config

axolotl version: 0.12.2

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

load_in_8bit: false
load_in_4bit: false

datasets:
  - path: train.jsonl
    type: chat_template

dataset_prepared_path: last_run_prepared
val_set_size: 0.01
output_dir: ./outputs/out

adapter: 
lora_model_dir:

sequence_len: 5120
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: false

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

wandb_project: wizl-base-m
wandb_entity:
wandb_watch:
wandb_name: 8b-base-fsv
wandb_log_model:

hub_model_id: jadechoi/wizl_base_7b-fsv

gradient_accumulation_steps: 4
micro_batch_size: 16
num_epochs: 3
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 2e-5

bf16: true
fp16:
tf32: false

gradient_checkpointing:
logging_steps: 1
flash_attention: true
eager_attention:

warmup_ratio: 0.05
evals_per_epoch: 0
saves_per_epoch: 1
weight_decay: 0.01

fsdp:
  - full_shard
  - auto_wrap

fsdp_config:
  fsdp_state_dict_type: FULL_STATE_DICT
  fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer
  fsdp_activation_checkpointing: true

# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

wizl_base_7b-fsv

This model is a fine-tuned version of Qwen/Qwen2.5-Coder-7B-Instruct on the train.jsonl dataset.

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• total_eval_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: 11
• training_steps: 220

Training results

Framework versions

• Transformers 4.55.2
• Pytorch 2.6.0+cu124
• Datasets 4.0.0
• Tokenizers 0.21.4