RichardErkhov/yujiepan_-_phi-3-tiny-random-gguf

Quantization made by Richard Erkhov.

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phi-3-tiny-random - GGUF

• Model creator: https://huggingface.co/yujiepan/
• Original model: https://huggingface.co/yujiepan/phi-3-tiny-random/

Name	Quant method	Size
phi-3-tiny-random.Q2_K.gguf	Q2_K	0.0GB
phi-3-tiny-random.IQ3_XS.gguf	IQ3_XS	0.0GB
phi-3-tiny-random.IQ3_S.gguf	IQ3_S	0.0GB
phi-3-tiny-random.Q3_K_S.gguf	Q3_K_S	0.0GB
phi-3-tiny-random.IQ3_M.gguf	IQ3_M	0.0GB
phi-3-tiny-random.Q3_K.gguf	Q3_K	0.0GB
phi-3-tiny-random.Q3_K_M.gguf	Q3_K_M	0.0GB
phi-3-tiny-random.Q3_K_L.gguf	Q3_K_L	0.0GB
phi-3-tiny-random.IQ4_XS.gguf	IQ4_XS	0.0GB
phi-3-tiny-random.Q4_0.gguf	Q4_0	0.0GB
phi-3-tiny-random.IQ4_NL.gguf	IQ4_NL	0.0GB
phi-3-tiny-random.Q4_K_S.gguf	Q4_K_S	0.0GB
phi-3-tiny-random.Q4_K.gguf	Q4_K	0.0GB
phi-3-tiny-random.Q4_K_M.gguf	Q4_K_M	0.0GB
phi-3-tiny-random.Q4_1.gguf	Q4_1	0.0GB
phi-3-tiny-random.Q5_0.gguf	Q5_0	0.0GB
phi-3-tiny-random.Q5_K_S.gguf	Q5_K_S	0.0GB
phi-3-tiny-random.Q5_K.gguf	Q5_K	0.0GB
phi-3-tiny-random.Q5_K_M.gguf	Q5_K_M	0.0GB
phi-3-tiny-random.Q5_1.gguf	Q5_1	0.0GB
phi-3-tiny-random.Q6_K.gguf	Q6_K	0.0GB
phi-3-tiny-random.Q8_0.gguf	Q8_0	0.0GB

Original model description:

library_name: transformers pipeline_tag: text-generation inference: true widget: - text: Hello! example_title: Hello world group: Python

This model is randomly initialized, using the config from microsoft/Phi-3-mini-128k-instruct but with smaller size. Note the model is in float16.

Codes:

import transformers
import torch
import os
from huggingface_hub import create_repo, upload_folder

source_model_id = 'microsoft/Phi-3-mini-128k-instruct'
save_path = '/tmp/yujiepan/phi-3-tiny-random'
repo_id = 'yujiepan/phi-3-tiny-random'

config = transformers.AutoConfig.from_pretrained(
    source_model_id, trust_remote_code=True)
config.hidden_size = 16
config.intermediate_size = 32
config.num_attention_heads = 4
config.num_hidden_layers = 2
config.num_key_value_heads = 4
config.rope_scaling['long_factor'] = [1.0299, 1.0499]
config.rope_scaling['short_factor'] = [1.05, 1.05]

model = transformers.AutoModelForCausalLM.from_config(
    config, trust_remote_code=True)
model = model.to(torch.float16)
model.save_pretrained(save_path)

tokenizer = transformers.AutoTokenizer.from_pretrained(
    source_model_id, trust_remote_code=True)
tokenizer.save_pretrained(save_path)

result = transformers.pipelines.pipeline(
    'text-generation',
    model=model.float(), tokenizer=tokenizer)('Hello')
print(result)

os.system(f'ls -alh {save_path}')
create_repo(repo_id, exist_ok=True)
upload_folder(repo_id=repo_id, folder_path=save_path)

from transformers import AutoProcessor
AutoProcessor.from_pretrained(source_model_id, trust_remote_code=True).push_to_hub(repo_id)