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qnguyen3
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mathy-7b-3e

Text Generation
Transformers
Safetensors
qwen2
Generated from Trainer
conversational
text-generation-inference
Inference Endpoints
Model card Files Community
mathy-7b-3e
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---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-Math-7B
tags:
- generated_from_trainer
model-index:
- name: axolotl-outputs/Arcee-7B-Mathy-7B
  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: `0.5.0`
```yaml
base_model: Qwen/Qwen2.5-Math-7B

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true

strict: false

chat_template: chatml
datasets:

  - path: arcee-ai/orcamath_evol_85k
    type: chat_template
    split: train
    field_messages: conversations
    message_field_role: from
    message_field_content: value

  - path: allenai/tulu-3-sft-personas-math
    type: chat_template
    split: train[:10%]
    field_messages: messages
    message_field_role: role
    message_field_content: content

  - path: allenai/tulu-3-sft-personas-algebra
    type: chat_template
    split: train
    field_messages: messages
    message_field_role: role
    message_field_content: content

dataset_prepared_path: ./axolotl-datasets/math-evol-prepared
val_set_size: 0.02
output_dir: ./axolotl-outputs/Arcee-7B-Mathy-7B

sequence_len: 4096
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

wandb_project: "Arcee-Mathy-7B"
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_torch_fused #adamw_torch_fused # if you have OOM errors you can use adamw_8bit
lr_scheduler: linear
learning_rate: 5e-5

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: true
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 20
evals_per_epoch: 1
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.0

special_tokens:
  pad_token: <|endoftext|>
  eos_token: <|im_end|>

```

</details><br>

# axolotl-outputs/Arcee-7B-Mathy-7B

This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co./Qwen/Qwen2.5-Math-7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8577

## 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-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- 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: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4101        | 0.0106 | 1    | 1.6490          |
| 0.2037        | 0.9987 | 94   | 1.6728          |
| 0.177         | 1.9960 | 188  | 1.7276          |
| 0.1332        | 2.9920 | 282  | 1.8577          |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.3