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JasperV13
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Fhamator-SFT

Text Generation
Transformers
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Safetensors
gpt2
trl
sft
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text-generation-inference
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How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="JasperV13/Fhamator-SFT")
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JasperV13/Fhamator-SFT")
model = AutoModelForCausalLM.from_pretrained("JasperV13/Fhamator-SFT")
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Fhamator-SFT

This model is a fine-tuned version of JasperV13/Fhamator-30000 on an unknown 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: 2
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.0+cu121
  • Tokenizers 0.19.1
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