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Quantization made by Richard Erkhov.

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chat - GGUF

Name Quant method Size
chat.Q2_K.gguf Q2_K 0.08GB
chat.IQ3_XS.gguf IQ3_XS 0.08GB
chat.IQ3_S.gguf IQ3_S 0.08GB
chat.Q3_K_S.gguf Q3_K_S 0.08GB
chat.IQ3_M.gguf IQ3_M 0.09GB
chat.Q3_K.gguf Q3_K 0.09GB
chat.Q3_K_M.gguf Q3_K_M 0.09GB
chat.Q3_K_L.gguf Q3_K_L 0.1GB
chat.IQ4_XS.gguf IQ4_XS 0.1GB
chat.Q4_0.gguf Q4_0 0.1GB
chat.IQ4_NL.gguf IQ4_NL 0.1GB
chat.Q4_K_S.gguf Q4_K_S 0.1GB
chat.Q4_K.gguf Q4_K 0.11GB
chat.Q4_K_M.gguf Q4_K_M 0.11GB
chat.Q4_1.gguf Q4_1 0.11GB
chat.Q5_0.gguf Q5_0 0.11GB
chat.Q5_K_S.gguf Q5_K_S 0.11GB
chat.Q5_K.gguf Q5_K 0.12GB
chat.Q5_K_M.gguf Q5_K_M 0.12GB
chat.Q5_1.gguf Q5_1 0.12GB
chat.Q6_K.gguf Q6_K 0.13GB
chat.Q8_0.gguf Q8_0 0.17GB

Original model description:

library_name: transformers tags: - trl - sft - generated_from_trainer model-index: - name: chat results: []

chat

This model was trained from scratch on the None 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: 1
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 4
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: constant
  • lr_scheduler_warmup_ratio: 0.03
  • num_epochs: 4

Training results

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.1
  • Tokenizers 0.19.1
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GGUF
Model size
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gpt2
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