Feature Extraction
Transformers
Safetensors
opensci
llama-factory
full
Generated from Trainer
custom_code
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ontocord/1.7b-MixtureVitae-300BT-v1-decontaminated-16k", trust_remote_code=True, dtype="auto")Quick Links
1.7b-MixtureVitae-300BT-v1-decontaminated-16k
This model is a fine-tuned version of ontocord/1.7b-MixtureVitae-300BT-v1-decontaminated on the long_sft 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1.0
Training results
Framework versions
- Transformers 4.57.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.22.1
- Downloads last month
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ontocord/1.7b-MixtureVitae-300BT-v1-decontaminated-16k", trust_remote_code=True)