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Dynosaur_-_llama3-8b-math-sft-subtask-2-gguf
RichardErkhov/Dynosaur_-_llama3-8b-math-sft-subtask-2-gguf
Dynosaur_-_llama3-8b-math-sft-subtask-2-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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At a glance
- Access
- Public
- Created
- May 9, 2025
- Updated
- May 9, 2025
- SHA
- 8c845c18
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
llama3-8b-math-sft-subtask-2 - GGUF
- Model creator: https://huggingface.co/Dynosaur/
- Original model: https://huggingface.co/Dynosaur/llama3-8b-math-sft-subtask-2/
Original model description:
library_name: transformers license: llama3 base_model: Dynosaur/llama3-8b-math-sft tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer datasets:
- Dynosaur/math-sft-subtask-2 model-index:
- name: llama3-8b-math-sft-subtask-2 results: []
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
llama3-8b-math-sft-subtask-2
This model is a fine-tuned version of Dynosaur/llama3-8b-math-sft on the Dynosaur/math-sft-subtask-2 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
Training results
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
- Created
- May 9, 2025
- Updated
- May 9, 2025