hardlyworking/Games8B
18
<!-- 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"/> <details><summary>See axolotl config</summary>
axolotl version: 0.10.0.dev0
base_model: GreenerPastures/Bald-Beaver-8B
load_in_8bit: false
load_in_4bit: false
strict: false
chat_template: qwen3
datasets:
- path: NewEden/Joe-Games
type: completion
val_set_size: 0.05
output_dir: ./outputs/out
dataset_prepared_path: last_run_prepared
shuffle_merged_datasets: true
hub_model_id: hardlyworking/Games8B
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: Qwen8B
wandb_entity:
wandb_watch:
wandb_name: Qwen8B
wandb_log_model:
evals_per_epoch: 8
eval_table_size:
eval_max_new_tokens: 128
max_grad_norm: 1.0
gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 2
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:</details><br>
Games8B
This model is a fine-tuned version of GreenerPastures/Bald-Beaver-8B on the NewEden/Joe-Games dataset. It achieves the following results on the evaluation set:
- Loss: 1.9796
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
- trainbatchsize: 1
- evalbatchsize: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- totaltrainbatch_size: 4
- totalevalbatch_size: 4
- optimizer: Use OptimizerNames.ADAMWBNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
- lrschedulertype: cosine
- lrschedulerwarmup_steps: 13
- num_epochs: 2.0
Training results
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1