---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen3-32B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: shuttle-3.5-ckpts
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.9.0`
```yaml
# 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
```

</details><br>

# shuttle-3.5-ckpts

This model is a fine-tuned version of [Qwen/Qwen3-32B](https://huggingface.co/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