RichardErkhov/ahmedheakl_-_asm2asm-deepseek-1.3b-100k-x86-O0-2ep-arm-gnueabi-gcc-gguf

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asm2asm-deepseek-1.3b-100k-x86-O0-2ep-arm-gnueabi-gcc - GGUF

• —Model creator: https://huggingface.co/ahmedheakl/
• —Original model: https://huggingface.co/ahmedheakl/asm2asm-deepseek-1.3b-100k-x86-O0-2ep-arm-gnueabi-gcc/

Original model description: --- libraryname: transformers license: other basemodel: deepseek-ai/deepseek-coder-1.3b-instruct tags:

• —trl
• —sft
• —generatedfromtrainer model-index:
• —name: asm2asm-deepseek-1.3b-100k-x86-O0-2ep-arm-gnueabi-gcc 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. -->

asm2asm-deepseek-1.3b-100k-x86-O0-2ep-arm-gnueabi-gcc

This model is a fine-tuned version of deepseek-ai/deepseek-coder-1.3b-instruct 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: 0.0002
• —trainbatchsize: 1
• —evalbatchsize: 8
• —seed: 42
• —gradientaccumulationsteps: 4
• —totaltrainbatch_size: 4
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —lrschedulerwarmup_ratio: 0.03
• —num_epochs: 2

Training results

Framework versions

• —Transformers 4.44.2
• —Pytorch 2.4.1+cu118
• —Datasets 3.0.0
• —Tokenizers 0.19.1

Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more quants, at much higher speed, than I would otherwise be able to.