---
license: mit
language: en
---

## This is a pre-release model interface, training started on February 7, 2024, and the model will be released in the future. ##

The model adopts the Phi architecture, with 550 million parameters. It only supports English and does not support code writing.


The model's dataset is obtained by cleaning and deduplicating open-source datasets, with pre-training using approximately 30 billion instances.


If you are a native English speaker, you might find these sentences uncomfortable to read because the training of this model and the writing of this document were only completed by a very inexperienced Chinese high school student.


Anyway, this is a new attempt. It is trained on consumer-grade devices and without the guidance of professionals, so it's hard for us to expect it to perform exceptionally well.

But we hope this will be the beginning of a new great exploration.

(We have released a preview version on February 24, 2024, and you can run it using the following code：

```
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
import torch

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

tokenizer = AutoTokenizer.from_pretrained('/mnt/n/save/tokenizer')
model = AutoModelForCausalLM.from_pretrained('/mnt/n/save/modelgen4/checkpoint-37920').to(device)
tokenizer.pad_token = tokenizer.eos_token

txt = 'A person with a cold should immediately'

# greedy search
gen_conf = GenerationConfig(
    num_beams=1,
    do_sample=True,
    max_length=700,
    no_repeat_ngram_size=6,
    eos_token_id=tokenizer.eos_token_id,
    pad_token_id=tokenizer.pad_token_id,
    temperature=0.93,
    top_k=36,
    top_p=0.80
)

tokend = tokenizer.encode_plus(text=txt)
input_ids, attention_mask = torch.LongTensor([tokend.input_ids]).to(device), \
    torch.LongTensor([tokend.attention_mask]).to(device)

outputs = model.generate(
    inputs=input_ids,
    attention_mask=attention_mask,
    generation_config=gen_conf,
    
)

outs = tokenizer.decode(outputs[0].cpu().numpy(), clean_up_tokenization_spaces=True,)
print(outs)

```