See axolotl config

axolotl version: 0.9.0

# Weights and Biases logging config
wandb_project: shuttle-3.5
wandb_name: "3.5"

# Model architecture config
base_model: Qwen/Qwen3-32B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
chat_template: chatml

# Hugging Face saving config
hub_model_id: shuttleai/shuttle-3.5-ckpts
hub_strategy: all_checkpoints

# Model checkpointing config
output_dir: ./lora-out
saves_per_epoch: 10
save_safetensors: true
save_total_limit: 5

# Mixed precision training config
bf16: true
fp16: false
tf32: false

# Model loading config
load_in_8bit: false
load_in_4bit: true
strict: false

# Sequence config
sequence_len: 16384
s2_attention: false
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
train_on_inputs: false
group_by_length: false

# QLoRA adapter config
adapter: qlora
lora_r: 64
lora_alpha: 64
lora_dropout: 0.05
peft_use_dora: false
lora_target_modules:
    - gate_proj
    - down_proj
    - up_proj
    - q_proj
    - v_proj
    - k_proj
    - o_proj

# Dataset config
datasets:
    - path: ./dataset
      type: chat_template
val_set_size: 0.05
evals_per_epoch: 10
dataset_prepared_path: ./prepared-datasets
shuffle_merged_datasets: true

# Training hyperparameters
num_epochs: 1
gradient_accumulation_steps: 2
micro_batch_size: 2
eval_batch_size: 1
warmup_steps: 500
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-4
loraplus_lr_ratio: 8
cosine_min_lr_ratio: 0.1
weight_decay: 0.1
max_grad_norm: 1
logging_steps: 1

# Model optimization
gradient_checkpointing: unsloth
xformers_attention: false
flash_attention: true
sdp_attention: false
unsloth_cross_entropy_loss: true
unsloth_lora_mlp: false
unsloth_lora_qkv: false
unsloth_lora_o: false

# Loss monitoring config
early_stopping_patience: false
loss_watchdog_threshold: 100.0
loss_watchdog_patience: 3

# Debug config
debug: false
seed: 42

deepspeed: deepspeed_configs/zero2.json

shuttle-3.5-ckpts

This model is a fine-tuned version of Qwen/Qwen3-32B on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.9783

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: 2
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• gradient_accumulation_steps: 2
• total_train_batch_size: 4
• 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: 500
• num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss
7.5468	0.0006	1	7.0761
4.9993	0.1006	160	5.6051
3.358	0.2011	320	2.5960
1.809	0.3017	480	1.3915
2.088	0.4023	640	1.1270
1.8377	0.5028	800	1.0472
1.8002	0.6034	960	1.0100
1.7863	0.7040	1120	0.9924
1.4572	0.8045	1280	0.9861
1.8509	0.9051	1440	0.9783

Framework versions

• PEFT 0.15.2
• Transformers 4.51.3
• Pytorch 2.5.1+cu124
• Datasets 3.5.0
• Tokenizers 0.21.1