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qwen3-14b-triton-v1

Text Generation
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qwen3
Generated from Trainer
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qwen3-14b-triton-v1
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---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-14B-Base
tags:
- generated_from_trainer
datasets:
- axolotl-ai-internal/gpumode-py2triton-reasoning-v2
model-index:
- name: outputs/out
  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: `0.10.0.dev0`
```yaml
base_model: Qwen/Qwen3-14B-Base

plugins:
  - axolotl.integrations.liger.LigerPlugin
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true

chat_template: qwen3 
datasets:
  - path: axolotl-ai-internal/gpumode-py2triton-reasoning-v2
    type: chat_template
    split: train
    split_thinking: true
    eot_tokens: ["<|im_end|>"]

dataset_prepared_path: last_run_prepared
val_set_size: 0.005
output_dir: ./outputs/out
save_only_model: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

wandb_project: qwen3-14b-grpo-triton
wandb_entity: axolotl-ai
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_torch_fused
max_grad_norm: 0.1
neftune_noise_alpha: 10
lr_scheduler: cosine
learning_rate: 3e-6

bf16: true
tf32: true

gradient_checkpointing: offload
gradient_checkpointing_kwargs:
  use_reentrant: false
logging_steps: 1
flash_attention: true

warmup_steps: 100
evals_per_epoch: 5
saves_per_epoch: 1
weight_decay: 0.01
deepspeed: deepspeed_configs/zero1.json

```

</details><br>

# outputs/out

This model is a fine-tuned version of [Qwen/Qwen3-14B-Base](https://huggingface.co/Qwen/Qwen3-14B-Base) on the axolotl-ai-internal/gpumode-py2triton-reasoning-v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2053

## 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: 3e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100
- num_epochs: 3.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4288        | 0.0039 | 1    | 0.5326          |
| 0.289         | 0.2    | 51   | 0.3414          |
| 0.2091        | 0.4    | 102  | 0.2622          |
| 0.2009        | 0.6    | 153  | 0.2362          |
| 0.1848        | 0.8    | 204  | 0.2248          |
| 0.1654        | 1.0    | 255  | 0.2186          |
| 0.1803        | 1.2    | 306  | 0.2165          |
| 0.1642        | 1.4    | 357  | 0.2116          |
| 0.1714        | 1.6    | 408  | 0.2094          |
| 0.164         | 1.8    | 459  | 0.2074          |
| 0.1488        | 2.0    | 510  | 0.2069          |
| 0.1676        | 2.2    | 561  | 0.2069          |
| 0.153         | 2.4    | 612  | 0.2059          |
| 0.1621        | 2.6    | 663  | 0.2056          |
| 0.1568        | 2.8    | 714  | 0.2055          |
| 0.1433        | 3.0    | 765  | 0.2053          |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1