metadata
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 THUDM/chatglm3-6b-128k 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 = 'THUDM/chatglm3-6b-128k'
tiny_random_name = 'chatglm3-tiny-random'
save_path = f'/tmp/yujiepan/{tiny_random_name}'
repo_id = f'yujiepan/{tiny_random_name}'
config = transformers.AutoConfig.from_pretrained(
source_model_id, trust_remote_code=True)
config.hidden_size = 4
config.ffn_hidden_size = 6
config.num_attention_heads = 4
config.kv_channels = 2
config.num_layers = 2
config.torch_dtype = torch.float16
model = transformers.AutoModelForCausalLM.from_config(
config, trust_remote_code=True, torch_dtype=torch.float16)
model = model.half()
tokenizer = transformers.AutoTokenizer.from_pretrained(
source_model_id, trust_remote_code=True)
# result = transformers.pipelines.pipeline(
# 'text-generation',
# model=model, tokenizer=tokenizer,
# device=0,
# max_new_tokens=16,
# )('Hello')
# print(result)
model = model.cuda()
response, history = model.chat(tokenizer, "Hi", history=[], max_length=32)
print(response)
model.save_pretrained(save_path)
tokenizer.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)