RichardErkhov/BueormLLC_-_GPT2Coder-gguf

Quantization made by Richard Erkhov.

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GPT2Coder - GGUF

• Model creator: https://huggingface.co/BueormLLC/
• Original model: https://huggingface.co/BueormLLC/GPT2Coder/

Name	Quant method	Size
GPT2Coder.Q2_K.gguf	Q2_K	0.08GB
GPT2Coder.Q3_K_S.gguf	Q3_K_S	0.08GB
GPT2Coder.Q3_K.gguf	Q3_K	0.09GB
GPT2Coder.Q3_K_M.gguf	Q3_K_M	0.09GB
GPT2Coder.Q3_K_L.gguf	Q3_K_L	0.1GB
GPT2Coder.IQ4_XS.gguf	IQ4_XS	0.1GB
GPT2Coder.Q4_0.gguf	Q4_0	0.1GB
GPT2Coder.IQ4_NL.gguf	IQ4_NL	0.1GB
GPT2Coder.Q4_K_S.gguf	Q4_K_S	0.1GB
GPT2Coder.Q4_K.gguf	Q4_K	0.11GB
GPT2Coder.Q4_K_M.gguf	Q4_K_M	0.11GB
GPT2Coder.Q4_1.gguf	Q4_1	0.11GB
GPT2Coder.Q5_0.gguf	Q5_0	0.11GB
GPT2Coder.Q5_K_S.gguf	Q5_K_S	0.11GB
GPT2Coder.Q5_K.gguf	Q5_K	0.12GB
GPT2Coder.Q5_K_M.gguf	Q5_K_M	0.12GB
GPT2Coder.Q5_1.gguf	Q5_1	0.12GB
GPT2Coder.Q6_K.gguf	Q6_K	0.13GB
GPT2Coder.Q8_0.gguf	Q8_0	0.17GB

Original model description:

license: mit datasets: - agicorp/python_code_instructions_18k_alpaca language: - en base_model: - openai-community/gpt2 pipeline_tag: text-generation library_name: transformers tags: - code

Model Card

GPT2Coder is a language model that uses openAI's GPT2 model architecture, the model was pre-trained on multiple code data focused on python and languages ​​​​such as Spanish and English.

• It is a pre-trained model in a medium amount of code, so it is not recommended to use it like this, but it is functional and serves uses such as fine tuning and other tasks.

Model Details

• Developed by: BueormAI
• Shared by: BueormLLC
• Model type: Transformer
• Language(s) (NLP): English (en), Spanish (es)
• License: MiT
• Finetuned from model: GPT2 Architecture

Bias, Risks, and Limitations

The model can generate unexpected code and output, in addition to offensive texts and non-functional code.

Recommendations

We recommend using the model with caution and handling its outputs with discretion as they may turn out to be non-functional outputs and harmful and dangerous code.

Training Details

Training Hyperparameters

• Training regime: fp16 mixed precision
• Max_lenght: 1024 tokens
• pretrain epochs: 1 epochs
• finetuning epochs: 2 epochs

Environmental Impact

• Hardware Type: GPU P100
• Hours used: 18 hours
• Cloud Provider: Kaggle

By Bueorm

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