You can deploy this model and start earning money today!

RichardErkhov/
susnato_-_phi-2-gguf

Name: susnato_-_phi-2-gguf
Availability: InStock

endpoints_compatible

gguf

template

Views

No views yet

Quantization made by Richard Erkhov.

Github

Discord

Request more models

phi-2 - GGUF

Model creator: https://huggingface.co/susnato/
Original model: https://huggingface.co/susnato/phi-2/

Name	Quant method	Size
phi-2.Q2_K.gguf	Q2_K	1.03GB
phi-2.IQ3_XS.gguf	IQ3_XS	1.12GB
phi-2.IQ3_S.gguf	IQ3_S	1.16GB
phi-2.Q3_K_S.gguf	Q3_K_S	1.16GB
phi-2.IQ3_M.gguf	IQ3_M	1.23GB
phi-2.Q3_K.gguf	Q3_K	1.33GB
phi-2.Q3_K_M.gguf	Q3_K_M	1.33GB
phi-2.Q3_K_L.gguf	Q3_K_L	1.47GB
phi-2.IQ4_XS.gguf	IQ4_XS	1.43GB
phi-2.Q4_0.gguf	Q4_0	1.49GB
phi-2.IQ4_NL.gguf	IQ4_NL	1.5GB
phi-2.Q4_K_S.gguf	Q4_K_S	1.51GB
phi-2.Q4_K.gguf	Q4_K	1.62GB
phi-2.Q4_K_M.gguf	Q4_K_M	1.62GB
phi-2.Q4_1.gguf	Q4_1	1.65GB
phi-2.Q5_0.gguf	Q5_0	1.8GB
phi-2.Q5_K_S.gguf	Q5_K_S	1.8GB
phi-2.Q5_K.gguf	Q5_K	1.87GB
phi-2.Q5_K_M.gguf	Q5_K_M	1.87GB
phi-2.Q5_1.gguf	Q5_1	1.95GB
phi-2.Q6_K.gguf	Q6_K	2.13GB
phi-2.Q8_0.gguf	Q8_0	2.75GB

Original model description:

license: mit license_name: microsoft-research-license license_link: LICENSE

DISCLAIMER: I don't own the weights to this model, this is a property of Microsoft and taken from their official repository : microsoft/phi-2. The sole purpose of this repository is to use this model through the transformers API or to load and use the model using the HuggingFace transformers library.

Usage

First make sure you have the latest version of the transformers installed.

pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers

Then use the transformers library to load the model from the library itself

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("susnato/phi-2")
tokenizer = AutoTokenizer.from_pretrained("susnato/phi-2")

inputs = tokenizer('''def print_prime(n):
   """
   Print all primes between 1 and n
   """''', return_tensors="pt", return_attention_mask=False)

outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)

RichardErkhov/susnato_-_phi-2-gguf

Original model description:

license: mit license_name: microsoft-research-license license_link: LICENSE

Usage

RichardErkhov/
susnato_-_phi-2-gguf