---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: outputs/Qwen_numina_raft2_orig_eos
  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: `0.8.0.dev0`
```yaml
base_model: outputs/Qwen_numina_raft1_orig_eos
trust_remote_code: false

load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: qwen_25
datasets:
  - path: data/raft_train_iter2_0_10000
    type: chat_template
    field_messages: conversations
    message_field_role: role
    message_field_content: content
    roles:
      user: ["human", "user"]
      assistant: ["gpt", "assistant", "ai"]
      system: ["system"]

dataset_prepared_path:
val_set_size: 0.0
output_dir: ./outputs/Qwen_numina_raft2_orig_eos

sequence_len: 8192
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true

# wandb_project: huggingface
# wandb_entity: zzzzzaa
# wandb_watch:
# wandb_name: qwen_test
# wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 1
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 1e-5

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

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

warmup_ratio: 0.05
saves_per_epoch: 1
evals_per_epoch: 0
debug:
weight_decay: 0.01
fsdp:
fsdp_config:
# special_tokens:
#   bos_token: "<|im_start|>"
#   eos_token: "<|im_end|>"
#   pad_token: "<|endoftext|>"


plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true

```

</details><br>

# outputs/Qwen_numina_raft2_orig_eos

This model was trained from scratch on the None 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Use paged_adamw_32bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 1.0

### Training results



### Framework versions

- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0