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yujiepan/gemma-tiny-random
gemma-tiny-random is a text generation model from yujiepan. Use it when you need the model to write or continue text. It is set up for transformers.
This model is randomly initialized, using the config from [https://huggingface.co/google/gemma-7b-it] but with smaller size. Note the model is in float16.
Downloads · 30 days
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.json17.6 MB · 68%
From the Hugging Face model README
This model is randomly initialized, using the config from [https://huggingface.co/google/gemma-7b-it] but with smaller size. Note the model is in float16.
Codes:
from transformers import pipeline
from huggingface_hub import create_repo, upload_folder
import torch
import transformers
import os
model_id = 'google/gemma-7b-it'
save_path = '/tmp/yujiepan/gemma-tiny-random'
repo_id = 'yujiepan/gemma-tiny-random'
config = transformers.AutoConfig.from_pretrained(model_id)
config.hidden_size = 8
config.head_dim = 2
config.intermediate_size = 16
config.num_attention_heads = 4
config.num_hidden_layers = 2
config.num_key_value_heads = 2
print(config)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
tokenizer.save_pretrained(save_path)
model = transformers.AutoModelForCausalLM.from_config(config, torch_dtype=torch.float16)
model = model.half()
pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, do_sample=False, device='cuda')
print(pipe('Hello World!'))
model.save_pretrained(save_path)
# ovmodel = OVModelForCausalLM.from_pretrained(save_path, export=True)
# ovmodel = ovmodel.half()
# ovmodel.save_pretrained(save_path)
os.system(f'ls -alh {save_path}')
create_repo(repo_id, exist_ok=True)
upload_folder(repo_id=repo_id, folder_path=save_path)