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---
library_name: transformers
license: cc-by-nc-4.0
base_model: Salesforce/xgen-small-4B-base-r
tags:
- axolotl
- generated_from_trainer
datasets:
- Mielikki/Erebus-87k
model-index:
- name: 4Bcpt
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: `0.11.0.dev0`
```yaml
base_model: Salesforce/xgen-small-4B-base-r
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: Mielikki/Erebus-87k
type: completion
field: body
output_dir: ./outputs/out
dataset_prepared_path: last_run_prepared
shuffle_merged_datasets: true
hub_model_id: hardlyworking/4Bcpt
hub_strategy: "all_checkpoints"
push_dataset_to_hub:
hf_use_auth_token: true
plugins:
- axolotl.integrations.liger.LigerPlugin
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: false
cut_cross_entropy: true
sequence_len: 32768
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
wandb_project: New4B
wandb_entity:
wandb_watch:
wandb_name: New4Bcpt
wandb_log_model:
evals_per_epoch:
eval_table_size:
eval_max_new_tokens:
gradient_accumulation_steps: 2
micro_batch_size: 8
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: offload
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
s2_attention:
deepspeed:
warmup_ratio: 0.05
saves_per_epoch: 1
debug:
weight_decay: 0.01
fsdp:
fsdp_config:
special_tokens:
pad_token: <|endoftext|>
```
</details><br>
# 4Bcpt
This model is a fine-tuned version of [Salesforce/xgen-small-4B-base-r](https://huggingface.co/Salesforce/xgen-small-4B-base-r) on the Mielikki/Erebus-87k 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB 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: 18
- training_steps: 374
### Training results
### Framework versions
- Transformers 4.53.1
- Pytorch 2.6.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.2
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