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SimpleBerry/LLaMA-O1-Base-1127

Model

should probably proofread and complete it, then remove this comment. -->

18

3 commits

1 linked in READMEs

updated Dec 3, 2024

See the code

README

SimpleBerry/LLaMA-O1-Base-1127

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on the longcot_pt dataset.

Do not use this model without supervised training, please use LLaMA-O1-Supervised-1129 for directly usage.

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: 5e-05
  • train_batch_size: 1
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 24
  • total_train_batch_size: 24
  • total_eval_batch_size: 192
  • 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
  • num_epochs: 4.0

Training results

Framework versions

  • Transformers 4.46.2
  • Pytorch 2.3.1
  • Datasets 3.1.0
  • Tokenizers 0.20.1
conversational
endpoints_compatible
full
generated_from_trainer
llama
llama-factory
safetensors
text-generation
text-generation-inference
transformers

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SimpleBerry/LLaMA-O1

Large Reasoning Models

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SimpleBerry/LLaMA-O1-Base-1127

Model

should probably proofread and complete it, then remove this comment. -->

18

3 commits

1 linked in READMEs

updated Dec 3, 2024

See the code

README

SimpleBerry/LLaMA-O1-Base-1127

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on the longcot_pt dataset.

Do not use this model without supervised training, please use LLaMA-O1-Supervised-1129 for directly usage.

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: 5e-05
  • train_batch_size: 1
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 24
  • total_train_batch_size: 24
  • total_eval_batch_size: 192
  • 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
  • num_epochs: 4.0

Training results

Framework versions

  • Transformers 4.46.2
  • Pytorch 2.3.1
  • Datasets 3.1.0
  • Tokenizers 0.20.1
conversational
endpoints_compatible
full
generated_from_trainer
llama
llama-factory
safetensors
text-generation
text-generation-inference
transformers

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license: mit

0

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1

HuggingFaceH4/sft-llava-1.5-7b-hf

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This model is a fine-tuned version of [PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T](https://hu…

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Yuan-Li-FNLP/R3-RAG-CS-Llama

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SimpleBerry/LLaMA-O1

Large Reasoning Models

799

README badge