Tibogoss/Qwen3-8B-v3-ge

See axolotl config

axolotl version: 0.13.0.dev0

base_model: Qwen/Qwen3-8B
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name

quantization_config:
  load_in_4bit: false
  bnb_4bit_quant_type: "nf4"
  bnb_4bit_compute_dtype: "bfloat16"
  bnb_4bit_use_double_quant: false
strict: false

train_on_inputs: false

datasets:
  - path: /workspace/outputs/training_data/
    ds_type: json
    data_files: 
        - ge_v2.json
    type: chat_template
dataset_prepared_path:
val_set_size: 0.00
output_dir: /workspace/outputs/FT_v3_ge

sequence_len: 1024
sample_packing: true
eval_sample_packing: true


adapter: lora
lora_model_dir:
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true

use_wandb: true
wandb_project: "Tiboobs-GE"
wandb_name: SFT_GE

gradient_accumulation_steps: 2
micro_batch_size: 1
num_epochs: 3 # maybe more
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 0.0002

bf16: auto
tf32: true

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.1
#evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.0
#fsdp:
#  - full_shard
#  - auto_wrap
#fsdp_config:
#  fsdp_limit_all_gathers: true
#  fsdp_sync_module_states: true
#  fsdp_offload_params: true
#  fsdp_use_orig_params: false
#  fsdp_cpu_ram_efficient_loading: true
#  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
#  fsdp_transformer_layer_cls_to_wrap: Qwen3DecoderLayer
#  fsdp_state_dict_type: FULL_STATE_DICT
#  fsdp_sharding_strategy: FULL_SHARD
special_tokens:

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

workspace/outputs/FT_v3_ge

This model is a fine-tuned version of Qwen/Qwen3-8B on the None 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: 0.0002
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 7
• gradient_accumulation_steps: 2
• total_train_batch_size: 14
• total_eval_batch_size: 7
• 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: 12
• training_steps: 126

Training results

Framework versions

• PEFT 0.17.1
• Transformers 4.56.1
• Pytorch 2.7.1+cu126
• Datasets 4.0.0
• Tokenizers 0.22.1