See axolotl config
axolotl version: 0.10.0
adapter: qlora
base_model: Qwen/Qwen3-14B
bf16: false
chat_template: qwen3
dataloader_num_workers: 2
dataloader_pin_memory: true
dataloader_prefetch_factor: 8
datasets:
- eot_tokens:
- <|im_end|>
path: winglian/pirate-ultrachat-10k
split: train
type: chat_template
embeddings_skip_upcast: true
fp16: true
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
learning_rate: 0.00019
load_in_4bit: true
logging_steps: 1
lora_alpha: 64
lora_mlp_kernel: true
lora_o_kernel: true
lora_qkv_kernel: true
lora_r: 32
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- gate_proj
- down_proj
- up_proj
lr_scheduler: cosine
max_grad_norm: 0.1
micro_batch_size: 1
num_epochs: 1
optimizer: paged_adamw_8bit
output_dir: ./outputs/qwen-sft-pirate-rrr
plugins:
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
sample_packing: true
saves_per_epoch: 2
sequence_len: 4096
warmup_steps: 5
xformers_attention: true
outputs/qwen-sft-pirate-rrr
This model is a fine-tuned version of Qwen/Qwen3-14B on the winglian/pirate-ultrachat-10k 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: 0.00019
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT 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: 5
- training_steps: 25
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.15.2
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
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