elibury/wasmgiver-round1

See axolotl config

axolotl version: 0.16.0.dev0

adapter: lora
base_model: Qwen/Qwen3.5-27B
bf16: auto
datasets:
- path: elibury/wasmgiver-training
  type: alpaca
gradient_accumulation_steps: 8
learning_rate: 0.0001
load_in_8bit: true
lora_alpha: 64
lora_dropout: 0.05
lora_r: 32
lora_target_modules:
- q_proj
- v_proj
- k_proj
- o_proj
- gate_proj
- down_proj
- up_proj
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: ./outputs/mymodel
sequence_len: 2048
train_on_inputs: false

outputs/mymodel

This model is a fine-tuned version of Qwen/Qwen3.5-27B on the elibury/wasmgiver-training 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: 0.0001
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.ADAMW_BNB 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: 386

Training results

Framework versions

• PEFT 0.18.1
• Transformers 5.5.0
• Pytorch 2.11.0+cu126
• Datasets 4.5.0
• Tokenizers 0.22.1