---
library_name: transformers
license: apache-2.0
base_model: axolotl-ai-co/gpt-oss-20b-dequantized
tags:
- generated_from_trainer
datasets:
- HuggingFaceH4/Multilingual-Thinking
model-index:
- name: outputs/gpt-oss-20b/
  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. -->

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<details><summary>See axolotl config</summary>

axolotl version: `0.12.0`
```yaml
# the original mxfp4 quantized model is not supported with FSDP cpu_ram_efficient_loading
# FSDP cpu_ram_efficient_loading is used to reduce the initial CPU memory usage when loading the model
base_model: axolotl-ai-co/gpt-oss-20b-dequantized

use_kernels: false

dp_shard_size: 8  # requires 2x8xH100 nodes

plugins:
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin

experimental_skip_move_to_device: true  # prevent OOM by NOT putting model to GPU before sharding

datasets:
  - path: HuggingFaceH4/Multilingual-Thinking
    type: chat_template
    field_thinking: thinking
    template_thinking_key: thinking

dataset_prepared_path: last_run_prepared
val_set_size: 0
output_dir: ./outputs/gpt-oss-20b/
#save_only_model: true

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

wandb_project: gpt-oss-20b
wandb_name: fft-20b

gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 1

optimizer: adamw_torch_fused  # 8bit optimizers do not work with FSDP2 offload
lr_scheduler: constant_with_warmup
learning_rate: 2e-5
load_best_model_at_end: false

bf16: true
tf32: true

flash_attention: true
attn_implementation: kernels-community/vllm-flash-attn3

gradient_checkpointing: true
activation_offloading: true

logging_steps: 1
saves_per_epoch: 1

warmup_ratio: 0.03

special_tokens:
eot_tokens:
  - "<|end|>"

#deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json
fsdp_version: 2
fsdp_config:
  offload_params: true
  state_dict_type: SHARDED_STATE_DICT
  auto_wrap_policy: TRANSFORMER_BASED_WRAP
  transformer_layer_cls_to_wrap: GptOssDecoderLayer
  reshard_after_forward: true
  cpu_ram_efficient_loading: true
```

</details><br>

# outputs/gpt-oss-20b/

This model is a fine-tuned version of [axolotl-ai-co/gpt-oss-20b-dequantized](https://huggingface.co/axolotl-ai-co/gpt-oss-20b-dequantized) on the HuggingFaceH4/Multilingual-Thinking 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_batch_size: 32
- 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: constant_with_warmup
- training_steps: 8

### Training results



### Framework versions

- Transformers 4.55.0
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4