---
license: apache-2.0
---
# 芝麻 Zhima

## 介绍
**芝麻**是一个专注于中文现代诗创作的LLM，能根据用户指令用标题、摘要或关键词生成原创中文现代诗。

芝麻这个名字来源于志摩（徐志摩）的谐音。徐志摩（1897-1931）是一位著名的中国现代诗人。

我们使用[AI-Generated_Chinese_Modern_Poetry](https://huggingface.co/datasets/Hyaline/AI-Generated_Chinese_Modern_Poetry)和[chinese_modern_poetry](https://huggingface.co/datasets/Iess/chinese_modern_poetry)在8张A800上全参数训练了24个小时，基座模型使用[Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)。

## 快速开始
```
pip install transformers
```

```
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Hyaline/Zhima-0.5B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "使用以下标题写一首现代诗：向山谷吹来的风"
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

print(response)

```


# English
## Introduction
**Zhima** is an LLM that focuses on Chinese modern poetry creation and can generate original Chinese modern poems based on user instructions using titles, summaries, or keywords. 

The name Zhima comes from the homophone of Zhimo (Xu Zhimo). Xu Zhimo (1897-1931) is a famous Chinese modern poet.

We use [AI-Generated_Chinese_Modern_Poetry](https://huggingface.co/datasets/Hyaline/AI-Generated_Chinese_Modern_Poetry) and [chinese_modern_poetry](https://huggingface.co/datasets/Iess/chinese_modern_poetry) to conduct full-parameter training for 24 hours on 8 A800 GPUs, with [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) as the base model.

## Quick Start
```
pip install transformers
```

```
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Hyaline/Zhima-0.5B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "使用以下标题写一首现代诗：向山谷吹来的风"
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

print(response)

```