output-fizz

This model is a fine-tuned version of Qwen/Qwen3.5-9B.

W&B run: https://wandb.ai/cooawoo-personal/huggingface/runs/r719jwwe

Training procedure

Hyperparameters

Parameter Value
Learning rate 0.0002
LR scheduler SchedulerType.COSINE
Per-device batch size 2
Gradient accumulation 8
Effective batch size 16
Epochs 2
Max sequence length 4096
Optimizer OptimizerNames.PAGED_ADEMAMIX_8BIT
Weight decay 0.01
Warmup ratio 0.05
Max gradient norm 1.0
Precision bf16
Loss type nll
Chunked cross-entropy yes

LoRA configuration

Parameter Value
Rank (r) 128
Alpha 8
Dropout 0.05
Target modules attn.proj, down_proj, gate_proj, in_proj_a, in_proj_b, in_proj_qkv, in_proj_z, k_proj, linear_fc1, linear_fc2, o_proj, out_proj, q_proj, qkv, up_proj, v_proj
rsLoRA yes
Quantization 4-bit (nf4)

Dataset statistics

Dataset Samples Total tokens Trainable tokens
allura-forge/doubao-seed2.0-claude-distill-v1-qwen3.5-format 3,644 7,187,856 6,625,087
Training config
model_name_or_path: Qwen/Qwen3.5-9B
bf16: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
use_liger: true
use_cce: true
max_length: 4096
learning_rate: 0.0002
warmup_ratio: 0.05
weight_decay: 0.01
lr_scheduler_type: cosine
per_device_train_batch_size: 2
gradient_accumulation_steps: 8
optim: paged_ademamix_8bit
max_grad_norm: 1.0
use_peft: true
load_in_4bit: true
lora_r: 128
lora_alpha: 8
lora_dropout: 0.05
use_rslora: true
logging_steps: 1
disable_tqdm: true
save_strategy: steps
save_steps: 500
save_total_limit: 3
report_to: wandb
output_dir: output-fizz
data_config: data.yaml
prepared_dataset: prepared
num_train_epochs: 2
saves_per_epoch: 2
run_name: qwen35-9b-qlora-fizz
Data config
datasets:
- path: allura-forge/doubao-seed2.0-claude-distill-v1-qwen3.5-format
  type: conversational
  truncation_strategy: drop
shuffle_datasets: true
shuffle_combined: true
shuffle_seed: 42
eval_split: 0.0
split_seed: 42
assistant_only_loss: true

Framework versions

  • PEFT 0.18.1
  • Loft: 0.1.0
  • Transformers: 5.2.0
  • Pytorch: 2.10.0
  • Datasets: 4.5.0
  • Tokenizers: 0.22.2
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