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---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-3B
tags:
- axolotl
- generated_from_trainer
datasets:
- allenai/tulu-3-sft-mixture
model-index:
- name: II-Tulu-3B-SFT
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: `0.5.3.dev0`
```yaml
wandb_project: llm-training-platform
wandb_name: II-Tulu-3B-SFT
datasets:
- path: allenai/tulu-3-sft-mixture
split: train
type: chat_template
field_messages: messages
message_field_role: role
message_field_content: content
roles:
system:
- system
user:
- user
assistant:
- assistant
chat_template: qwen_25
sequence_len: 2048
base_model: Qwen/Qwen2.5-3B
output_dir: checkpoints/1357e2cd-76bc-46d5-a394-949b712427c7
dataset_prepared_path: checkpoints/1357e2cd-76bc-46d5-a394-949b712427c7/dataset_prepared
flash_attention: true
train_on_inputs: false
pad_to_sequence_len: true
eval_sample_packing: false
push_to_hub: true
bf16: auto
gradient_checkpointing: true
logging_steps: 10
hub_model_id: phunguyen01/II-Tulu-3B-SFT
learning_rate: 5.0e-06
micro_batch_size: 8
num_epochs: 2
seed: 42
gradient_accumulation_steps: 2
sample_packing: true
val_set_size: 0
```
</details><br>
# II-Tulu-3B-SFT
This model is a fine-tuned version of [Qwen/Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B) on the allenai/tulu-3-sft-mixture 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: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Use adamw_hf 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: 2
### Training results
### Framework versions
- Transformers 4.47.0
- Pytorch 2.4.0+cu121
- Datasets 3.1.0
- Tokenizers 0.21.0
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