---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- generated_from_trainer
datasets:
- pluralm-qwen25.parquet
model-index:
- name: model-output
  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: `0.13.0.dev0`
```yaml
base_model: Qwen/Qwen2.5-7B-Instruct
trust_remote_code: true

strict: false

# < -- Saving -- >
output_dir: ./model-output
saves_per_epoch: 4


# < -- Vram Savings -- >
#gradient_checkpointing: true
flash_attention: true

fsdp:
  - auto_wrap
  - full_shard
fsdp_config:
  fsdp_version: 2
  fsdp_offload_params: false
  fsdp_cpu_ram_efficient_loading: true
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer
  fsdp_state_dict_type: SHARDED_STATE_DICT
  fsdp_sharding_strategy: FULL_SHARD
  fsdp_reshard_after_forward: true
  fsdp_activation_checkpointing: true # will disable if doesnt work

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

# < -- Evals -- >
#evals_per_epoch
#eval_steps: 100
val_set_size: 0.0

# < -- Hparams -- >
warmup_steps: 5
sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

weight_decay: 0.0
gradient_accumulation_steps: 1
micro_batch_size: 2
num_epochs: 2
max_grad_norm: 0.01
optimizer: adamw_torch_8bit
lr_scheduler: cosine
learning_rate: 1e-5

## data 
datasets:
  - path: pluralm-qwen25.parquet
    ds_type: parquet
    type: 

shuffle_merged_datasets: true
dataset_prepared_path: last_run_prepared
remove_unused_columns: false

# < -- wandb -- >
wandb_project: PlurLM 7b
wandb_entity:
wandb_watch:
wandb_name: introject
wandb_log_model:

# < -- Misc -- >
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:


```

</details><br>

# model-output

This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the pluralm-qwen25.parquet 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: 1e-05
- 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_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: 5
- training_steps: 48

### Training results



### Framework versions

- Transformers 4.55.4
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4