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RichardErkhov/
realPCH_-_ko-solra-platusv3-koprompt-gguf

Name: realPCH_-_ko-solra-platusv3-koprompt-gguf
Availability: InStock

endpoints_compatible

gguf

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Quantization made by Richard Erkhov.

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ko-solra-platusv3-koprompt - GGUF

Model creator: https://huggingface.co/realPCH/
Original model: https://huggingface.co/realPCH/ko-solra-platusv3-koprompt/

Name	Quant method	Size
ko-solra-platusv3-koprompt.Q2_K.gguf	Q2_K	3.8GB
ko-solra-platusv3-koprompt.IQ3_XS.gguf	IQ3_XS	4.22GB
ko-solra-platusv3-koprompt.IQ3_S.gguf	IQ3_S	4.45GB
ko-solra-platusv3-koprompt.Q3_K_S.gguf	Q3_K_S	4.42GB
ko-solra-platusv3-koprompt.IQ3_M.gguf	IQ3_M	4.59GB
ko-solra-platusv3-koprompt.Q3_K.gguf	Q3_K	4.92GB
ko-solra-platusv3-koprompt.Q3_K_M.gguf	Q3_K_M	4.92GB
ko-solra-platusv3-koprompt.Q3_K_L.gguf	Q3_K_L	5.34GB
ko-solra-platusv3-koprompt.IQ4_XS.gguf	IQ4_XS	5.51GB
ko-solra-platusv3-koprompt.Q4_0.gguf	Q4_0	5.74GB
ko-solra-platusv3-koprompt.IQ4_NL.gguf	IQ4_NL	5.8GB
ko-solra-platusv3-koprompt.Q4_K_S.gguf	Q4_K_S	5.78GB
ko-solra-platusv3-koprompt.Q4_K.gguf	Q4_K	6.1GB
ko-solra-platusv3-koprompt.Q4_K_M.gguf	Q4_K_M	6.1GB
ko-solra-platusv3-koprompt.Q4_1.gguf	Q4_1	2.76GB
ko-solra-platusv3-koprompt.Q5_0.gguf	Q5_0	6.98GB
ko-solra-platusv3-koprompt.Q5_K_S.gguf	Q5_K_S	6.98GB
ko-solra-platusv3-koprompt.Q5_K.gguf	Q5_K	7.17GB
ko-solra-platusv3-koprompt.Q5_K_M.gguf	Q5_K_M	7.17GB
ko-solra-platusv3-koprompt.Q5_1.gguf	Q5_1	7.6GB
ko-solra-platusv3-koprompt.Q6_K.gguf	Q6_K	8.3GB
ko-solra-platusv3-koprompt.Q8_0.gguf	Q8_0	10.75GB

Original model description:

license: mit datasets:

kyujinpy/KOR-OpenOrca-Platypus-v3

Developed by chPark

Training Strategy

We fine-tuned this model based on yanolja/KoSOLAR-10.7B-v0.1

Run the model

python

from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "realPCH/ko_solra_merge"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
text = "[INST] Put instruction here. [/INST]"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

RichardErkhov/realPCH_-_ko-solra-platusv3-koprompt-gguf

Original model description:

Developed by chPark

Training Strategy

Run the model

RichardErkhov/
realPCH_-_ko-solra-platusv3-koprompt-gguf