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RichardErkhov/susnato_-_phi-2-gguf
susnato_-_phi-2-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
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.gguf37.4 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
phi-2 - GGUF
| 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 |
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.
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
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)