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RichardErkhov/
AtAndDev_-_ShortKing-1.4b-v0.1-gguf

Name: AtAndDev_-_ShortKing-1.4b-v0.1-gguf
Rating: 2.5 (58 reviews)

endpoints_compatible

gguf

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Quantization made by Richard Erkhov.

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ShortKing-1.4b-v0.1 - GGUF

Model creator: https://huggingface.co/AtAndDev/
Original model: https://huggingface.co/AtAndDev/ShortKing-1.4b-v0.1/

Name	Quant method	Size
ShortKing-1.4b-v0.1.Q2_K.gguf	Q2_K	0.53GB
ShortKing-1.4b-v0.1.IQ3_XS.gguf	IQ3_XS	0.59GB
ShortKing-1.4b-v0.1.IQ3_S.gguf	IQ3_S	0.61GB
ShortKing-1.4b-v0.1.Q3_K_S.gguf	Q3_K_S	0.61GB
ShortKing-1.4b-v0.1.IQ3_M.gguf	IQ3_M	0.66GB
ShortKing-1.4b-v0.1.Q3_K.gguf	Q3_K	0.71GB
ShortKing-1.4b-v0.1.Q3_K_M.gguf	Q3_K_M	0.71GB
ShortKing-1.4b-v0.1.Q3_K_L.gguf	Q3_K_L	0.77GB
ShortKing-1.4b-v0.1.IQ4_XS.gguf	IQ4_XS	0.74GB
ShortKing-1.4b-v0.1.Q4_0.gguf	Q4_0	0.77GB
ShortKing-1.4b-v0.1.IQ4_NL.gguf	IQ4_NL	0.78GB
ShortKing-1.4b-v0.1.Q4_K_S.gguf	Q4_K_S	0.78GB
ShortKing-1.4b-v0.1.Q4_K.gguf	Q4_K	0.85GB
ShortKing-1.4b-v0.1.Q4_K_M.gguf	Q4_K_M	0.85GB
ShortKing-1.4b-v0.1.Q4_1.gguf	Q4_1	0.85GB
ShortKing-1.4b-v0.1.Q5_0.gguf	Q5_0	0.92GB
ShortKing-1.4b-v0.1.Q5_K_S.gguf	Q5_K_S	0.92GB
ShortKing-1.4b-v0.1.Q5_K.gguf	Q5_K	0.98GB
ShortKing-1.4b-v0.1.Q5_K_M.gguf	Q5_K_M	0.98GB
ShortKing-1.4b-v0.1.Q5_1.gguf	Q5_1	1.0GB
ShortKing-1.4b-v0.1.Q6_K.gguf	Q6_K	1.08GB
ShortKing-1.4b-v0.1.Q8_0.gguf	Q8_0	1.4GB

Original model description:

license: cc-by-nc-4.0 datasets:

vicgalle/alpaca-gpt4 language:
en

Model Overview

Model license: cc-by-nc-4.0
This model is trained based on EleutherAI/pythia-1.4b-deduped model that is LoRA finetuned on vicgalle/alpaca-gpt4 dataset.

Prompt Template: `Alpaca`

<system_prompt>

### Instruction:
<user_message>

### Response:
<assistant_response>

Intended Use

THIS IS A TEST MODEL, IT IS NOT INTENDED FOR REAL APPLICATIONS BY ANY MEANS. HOWEVER, A NEW MODEL IS COMING IN THE SAME TOPIC.
This model series will be used for small but intense applications.

Training Details

This model took 2:31:23 to train in QLoRA on a single T4 GPU.

epochs: 1
train batch size: 12
eval batch size: 12
gradient accumulation steps: 1
maximum gradient normal: 0.3
learning rate: 2e-4
weight decay: 0.001
optimizer: paged_adamw_32bit
learning rate schedule: cosine
warmup ratio (linear): 0.03

RichardErkhov/AtAndDev_-_ShortKing-1.4b-v0.1-gguf