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RichardErkhov/Vishaltiwari2019_-_distilgpt2-finetuned-python_code_instructions_18k_alpaca-gguf

sourceHugging Faceupdated 2y agoView on Hugging Face
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Quantization made by Richard Erkhov.

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distilgpt2-finetuned-pythoncodeinstructions18kalpaca - GGUF

  • —Model creator: https://huggingface.co/Vishaltiwari2019/
  • —Original model: https://huggingface.co/Vishaltiwari2019/distilgpt2-finetuned-pythoncodeinstructions18kalpaca/
NameQuant methodSize
distilgpt2-finetuned-python_code_instructions_18k_alpaca.Q2_K.ggufQ2_K0.06GB
distilgpt2-finetuned-python_code_instructions_18k_alpaca.IQ3_XS.ggufIQ3_XS0.07GB
distilgpt2-finetuned-python_code_instructions_18k_alpaca.IQ3_S.ggufIQ3_S0.07GB
distilgpt2-finetuned-python_code_instructions_18k_alpaca.Q3_K_S.ggufQ3KS0.07GB
distilgpt2-finetuned-python_code_instructions_18k_alpaca.IQ3_M.ggufIQ3_M0.07GB
distilgpt2-finetuned-python_code_instructions_18k_alpaca.Q3_K.ggufQ3_K0.07GB
distilgpt2-finetuned-python_code_instructions_18k_alpaca.Q3_K_M.ggufQ3KM0.07GB
distilgpt2-finetuned-python_code_instructions_18k_alpaca.Q3_K_L.ggufQ3KL0.07GB
distilgpt2-finetuned-python_code_instructions_18k_alpaca.IQ4_XS.ggufIQ4_XS0.07GB
distilgpt2-finetuned-python_code_instructions_18k_alpaca.Q4_0.ggufQ4_00.08GB
distilgpt2-finetuned-python_code_instructions_18k_alpaca.IQ4_NL.ggufIQ4_NL0.08GB
distilgpt2-finetuned-python_code_instructions_18k_alpaca.Q4_K_S.ggufQ4KS0.08GB
distilgpt2-finetuned-python_code_instructions_18k_alpaca.Q4_K.ggufQ4_K0.08GB
distilgpt2-finetuned-python_code_instructions_18k_alpaca.Q4_K_M.ggufQ4KM0.08GB
distilgpt2-finetuned-python_code_instructions_18k_alpaca.Q4_1.ggufQ4_10.08GB
distilgpt2-finetuned-python_code_instructions_18k_alpaca.Q5_0.ggufQ5_00.09GB
distilgpt2-finetuned-python_code_instructions_18k_alpaca.Q5_K_S.ggufQ5KS0.09GB
distilgpt2-finetuned-python_code_instructions_18k_alpaca.Q5_K.ggufQ5_K0.09GB
distilgpt2-finetuned-python_code_instructions_18k_alpaca.Q5_K_M.ggufQ5KM0.09GB
distilgpt2-finetuned-python_code_instructions_18k_alpaca.Q5_1.ggufQ5_10.09GB
distilgpt2-finetuned-python_code_instructions_18k_alpaca.Q6_K.ggufQ6_K0.1GB
distilgpt2-finetuned-python_code_instructions_18k_alpaca.Q8_0.ggufQ8_00.12GB

Original model description: --- license: mit widget:

  • —text: My name is Julien and I like to example_title: Julien
  • —text: My name is Merve and my favorite exampletitle: Merve basemodel: distilgpt2 tags:
  • —generatedfromtrainer
  • —code model-index:
  • —name: distilgpt2-finetuned-pythoncodeinstructions18kalpaca results: [] datasets:
  • —iamtarun/pythoncodeinstructions18kalpaca language:
  • —en metrics:
  • —accuracy libraryname: transformers pipelinetag: text-generation ---

<!-- 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. -->

distilgpt2-finetuned-pythoncodeinstructions18kalpaca

This model is a fine-tuned version of distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set:

  • —Loss: 1.5063

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
  • —trainbatchsize: 8
  • —evalbatchsize: 8
  • —seed: 42
  • —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • —lrschedulertype: linear
  • —num_epochs: 3

Training results

Training LossEpochStepValidation Loss
1.72641.038611.5890
1.60462.077221.5214
1.53593.0115831.5063

Framework versions

  • —Transformers 4.39.3
  • —Pytorch 2.2.1+cu121
  • —Datasets 2.18.0
  • —Tokenizers 0.15.2