llamafactory-gemma4-rwkv-hrm-pt-text-freeze
This model is a fine-tuned version of
win10/Gemma4-HRM-RWKV-7-Untrained on the gemma4_fineweb_edu_10gb_local and the gemma4_tw_apple_daily_1b datasets.
Model description
Gemma4-HRM-RWKV-7 recovery checkpoint after a text-only PT run with the
RWKV/TICA/HRM replacement layers unfrozen.
This is not an instruction-tuned checkpoint. Short text-completion smoke tests
produce coherent continuation, while instruction-style greedy generation can
still repeat or answer poorly.
Intended uses & limitations
More information needed
Training and evaluation data
Training data used in this run:
gemma4_fineweb_edu_10gb_localgemma4_tw_apple_daily_1b
Total packed training data volume: 5,004,529,664 tokens, or 5.0045B
tokens. This is computed from the post-tokenization PT dataset size reported
by Trainer, 1,221,809 packed examples × cutoff length 4096.
Tokens actually consumed by this 300-step recovery run: 9,830,400 tokens,
or 0.00983B tokens (300 optimizer steps × total train batch size 8 ×
cutoff length 4096).
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0004
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- 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: cosine
- lr_scheduler_warmup_steps: 0.03
- training_steps: 300
Training results
Framework versions
- Transformers 5.6.0
- Pytorch 2.12.0+cu130
- Datasets 4.0.0
- Tokenizers 0.22.2
