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---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-14B
tags:
- generated_from_trainer
datasets:
- winglian/gpumode-py2triton-reasoning
model-index:
- name: outputs/out-fft
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: `0.8.0`
```yaml
base_model: Qwen/Qwen2.5-Coder-14B
strict: false
plugins:
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
- axolotl.integrations.liger.LigerPlugin
cut_cross_entropy: true
# liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
# gemma3 doesn't seem to play nice with ddp
ddp_find_unused_parameters: true
chat_template: qwen_25
datasets:
- path: winglian/gpumode-py2triton-reasoning
type: chat_template
dataset_prepared_path: last_run_prepared
val_set_size: 0.0
output_dir: ./outputs/out-fft
save_safetensors: true
save_only_model: true
sequence_len: 32768
sample_packing: true
pad_to_sequence_len: true
sequence_parallel_degree: 1
# unfrozen_parameters:
# - language_model.model
wandb_project: qwen25-kernel-llm
wandb_entity: axolotl-ai
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 3
num_epochs: 3
optimizer: adamw_8bit
lr_scheduler: rex
learning_rate: 3.0e-6
lr_groups:
- name: embeddings
lr: 3.0e-5
modules:
- lm_head
- embed_tokens
train_on_inputs: false
group_by_length: false
bf16: true
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
logging_steps: 1
flash_attention: true
warmup_ratio: 0.1
evals_per_epoch: 1
saves_per_epoch: 1
# deepspeed: deepspeed_configs/zero1.json
weight_decay: 0.0
deepspeed: deepspeed_configs/zero1.json
tokens:
- <think>
- </think>
special_tokens:
eos_token: <|im_end|>
fix_untrained_tokens:
- 151665
- 151666
```
</details><br>
# outputs/out-fft
This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-14B](https://huggingface.co/Qwen/Qwen2.5-Coder-14B) on the winglian/gpumode-py2triton-reasoning 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: 3e-06
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 24
- total_eval_batch_size: 24
- optimizer: Use OptimizerNames.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: 27
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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