Instructions to use p208p2002/bloom-1b1-zh-error-correction-dpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use p208p2002/bloom-1b1-zh-error-correction-dpo with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="p208p2002/bloom-1b1-zh-error-correction-dpo")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("p208p2002/bloom-1b1-zh-error-correction-dpo")
model = AutoModelForCausalLM.from_pretrained("p208p2002/bloom-1b1-zh-error-correction-dpo")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use p208p2002/bloom-1b1-zh-error-correction-dpo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "p208p2002/bloom-1b1-zh-error-correction-dpo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "p208p2002/bloom-1b1-zh-error-correction-dpo",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/p208p2002/bloom-1b1-zh-error-correction-dpo

SGLang

How to use p208p2002/bloom-1b1-zh-error-correction-dpo with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "p208p2002/bloom-1b1-zh-error-correction-dpo" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "p208p2002/bloom-1b1-zh-error-correction-dpo",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "p208p2002/bloom-1b1-zh-error-correction-dpo" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "p208p2002/bloom-1b1-zh-error-correction-dpo",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use p208p2002/bloom-1b1-zh-error-correction-dpo with Docker Model Runner:
```
docker model run hf.co/p208p2002/bloom-1b1-zh-error-correction-dpo
```

DPO Chinese Error Correction Model

使用DPO訓練的中文糾錯模型。

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM,AddedToken
import sys

mode_id = "p208p2002/bloom-1b1-zh-error-correction-dpo"
model: LlamaForCausalLM = AutoModelForCausalLM.from_pretrained("p208p2002/bloom-1b1-zh-error-correction-dpo")
tokenizer = AutoTokenizer.from_pretrained("p208p2002/bloom-1b1-zh-error-correction-dpo")

test_texts = [
    "為了潔約能源請隨守關閉沒有使用的電器",
    "今天新情很好",
    "你快樂我也很高心",
    "但不再算再找實習生了",
    "今天太陽很大要注意篩傷",
    "你要不要和我依起去台北",
    "清晨六點終太陽會升起",
    "傾城六點鐘太陽會升起",
    "鍋馬路時你應該要注意虹綠燈",
    "他正在學學彈吉他",
    "下樓梯請注意階梯",
    "此信件為系統自動發送之通知",
    "此信件為系統自動發送知通知",
    "如為誤傳也請立即刪除本郵件並通知寄件者"
]
for text in test_texts:
    inputs = tokenizer(
        f"{tokenizer.bos_token}{text} {tokenizer.eos_token}\n {tokenizer.bos_token}",
        return_tensors="pt",
        add_special_tokens=False
    )["input_ids"]

    out = model.generate(
        inputs,
        max_new_tokens=20,
    )
    decode_out = tokenizer.decode(out[0])

    input_text,output_text = decode_out.split("\n") 
    input_text = input_text.strip()
    output_text = output_text.strip()

    print("input :",input_text)
    print("output:",output_text)
    print('-'*30)

input: <s>為了潔約能源請隨守關閉沒有使用的電器 </s>
output: <s>為了節約能源請隨時關閉沒有使用的電器 </s>
------------------------------
input: <s>今天新情很好 </s>
output: <s>今天心情很好 </s>
------------------------------
input: <s>你快樂我也很高心 </s>
output: <s>你快樂我也很高興 </s>
------------------------------
input: <s>但不再算再找實習生了 </s>
output: <s>但不再去找實習生了 </s>
------------------------------
input: <s>今天太陽很大要注意篩傷 </s>
output: <s>今天太陽很大要注意一下 </s>
------------------------------
input: <s>你要不要和我依起去台北 </s>
output: <s>你要不要和我一起去台北 </s>
------------------------------
input: <s>清晨六點終太陽會升起 </s>
output: <s>清晨六點鐘太陽會升起 </s>
------------------------------
input: <s>傾城六點鐘太陽會升起 </s>
output: <s>凌晨六點鐘太陽會升起 </s>
------------------------------
input: <s>鍋馬路時你應該要注意虹綠燈 </s>
output: <s>過馬路時你應該要注意紅綠燈 </s>
------------------------------
input: <s>他正在學學彈吉他 </s>
output: <s>他正在學習彈吉他 </s>
------------------------------
input: <s>下樓梯請注意階梯 </s>
output: <s>下樓梯請注意階梯 </s>
------------------------------
input: <s>此信件為系統自動發送之通知 </s>
output: <s>此信件為系統自動發送之通知 </s>
------------------------------
input: <s>此信件為系統自動發送知通知 </s>
output: <s>此信件為系統自動發送通知 </s>
------------------------------
input: <s>如為誤傳也請立即刪除本郵件並通知寄件者 </s>
output: <s>如為誤傳也請立即刪除本郵件並通知寄件者 </s>
------------------------------
input : <s>為了潔約能源請隨守關閉沒有使用的電器 </s>
output: <s>為了節約能源請隨時關閉沒有使用的電器 </s>
------------------------------
input : <s>今天新情很好 </s>
output: <s>今天心情很好 </s>
------------------------------
input : <s>你快樂我也很高心 </s>
output: <s>你快樂我也很高興 </s>
------------------------------
input : <s>但不再算再找實習生了 </s>
output: <s>但不再去找實習生了 </s>
------------------------------
input : <s>今天太陽很大要注意篩傷 </s>
output: <s>今天太陽很大要注意一下 </s>
------------------------------
input : <s>你要不要和我依起去台北 </s>
output: <s>你要不要和我一起去台北 </s>
------------------------------
input : <s>清晨六點終太陽會升起 </s>
output: <s>清晨六點鐘太陽會升起 </s>
------------------------------
input : <s>傾城六點鐘太陽會升起 </s>
output: <s>凌晨六點鐘太陽會升起 </s>
------------------------------
input : <s>鍋馬路時你應該要注意虹綠燈 </s>
output: <s>過馬路時你應該要注意紅綠燈 </s>
------------------------------
input : <s>他正在學學彈吉他 </s>
output: <s>他正在學習彈吉他 </s>
------------------------------
input : <s>下樓梯請注意階梯 </s>
output: <s>下樓梯請注意階梯 </s>
------------------------------
input : <s>此信件為系統自動發送之通知 </s>
output: <s>此信件為系統自動發送之通知 </s>
------------------------------
input : <s>此信件為系統自動發送知通知 </s>
output: <s>此信件為系統自動發送通知 </s>
------------------------------
input : <s>如為誤傳也請立即刪除本郵件並通知寄件者 </s>
output: <s>如為誤傳也請立即刪除本郵件並通知寄件者 </s>
------------------------------

Downloads last month: 8

Safetensors

Model size

1B params

Tensor type

F32

Dataset used to train p208p2002/bloom-1b1-zh-error-correction-dpo

Collection including p208p2002/bloom-1b1-zh-error-correction-dpo

Useful Models

Collection

3 items • Updated Mar 26, 2024