ContaLLM-Beauty-8B-Instruct
ContaLLM-Beauty-8B-Instruct is a large Chinese vertical marketing model that can be customized to generate marketing text based on users' specific marketing needs, as well as keywords, topics, hashtags, marketing seasons, character settings, relevant materials, content length, etc, which uses LLM's capability and trained on existing high-quality marketing materials to help enterprises generate diverse and high-quality marketing content and increase marketing conversion rate.
Model description
- Model type: A model trained on a mix of publicly available, synthetic and human-annotated datasets.
- Language(s) (NLP): Primarily Chinese
- Industry: Beauty Makeup Industry Marketing
- License: Llama 3.1 Community License Agreement
- Finetuned from model: meta-llama/Llama-3.1-8B-Instruct
Model Stage
| Industry | Version | Llama 3.1 8B |
|---|---|---|
| Beauty | bf16 | ContaAI/ContaLLM-Beauty-8B-Instruct |
| Beauty | 8bit | ContaAI/ContaLLM-Beauty-8B-Instruct-8bit |
| Beauty | 4bit | ContaAI/ContaLLM-Beauty-8B-Instruct-4bit |
Using the model
Loading with HuggingFace
To load the model with HuggingFace, use the following snippet:
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ContaAI/ContaLLM-Beauty-8B-Instruct")
System Prompt
The model is a Chinese beauty marketing model, so we use this system prompt by default:
system_prompt = '่ฏทๆ นๆฎ็จๆทๆไพ็่ฅ้้ๆฑๅๅ
ถไปไฟกๆฏๅไธ็ฏ็พๅฆๆค่ค่กไธ็่ฅ้ๆจๆใ'
User Prompt
Users can enter the required marketing needs according to their own needs, non-required including keywords, topics, label marketing nodes, people, related materials, content length, which content length has three specifications, respectively, shorter, medium, longer. The details are as follows:
| Parameter name | Required | Meaning and optional range |
|---|---|---|
| ่ฅ้้ๆฑ | required | Fill in your marketing requirements, cannot be blank |
| ๅ ณ้ฎ่ฏ | optional | Fill in your marketing keywords, or remove this row from the prompt |
| ่ฏ้ข | optional | Fill in your marketing topic, or remove this row from the prompt |
| ๆ ็ญพ | optional | Fill in the hashtag, or remove this row from the prompt |
| ่ฅ้่็น | optional | Fill in the marketing season, such as Valentine's Day, Christmas, or remove this row from the prompt |
| ไบบ่ฎพ | optional | Fill in your character settings, or remove this row from the prompt |
| ็ธๅ ณ็ด ๆ | optional | Fill in the relevant materials for your marketing needs, or remove this row from the prompt |
| ๅ ๅฎน้ฟๅบฆ | optional | choices=['่พ้ฟ', 'ไธญ็ญ', '่พ็ญ'], choose what you need, or remove this row from the prompt |
Example:
user_prompt = """่ฅ้้ๆฑ๏ผ็พ็ฝๆฐดไนณๆจ่๏ผๆจๅนฟHBNๅ็ฝๆฐดไนณใ
ๅ
ณ้ฎ่ฏ๏ผHBNๅ็ฝๆฐดไนณ
่ฏ้ข๏ผ ๅไบซๆค่ค ๆไบฎ่ค่ฒ
ๆ ็ญพ๏ผ็ฑๆ
ใๆตชๆผซ
่ฏ้ข๏ผ ๅไบซๆค่ค ๆไบฎ่ค่ฒ
ไบบ่ฎพ๏ผ็พ็ฝๆฐดไนณๆจ่๏ผๆจๅนฟHBNๅ็ฝๆฐดไนณใ
็ธๅ
ณ็ด ๆ๏ผ็พ็ฝๆฐดไนณๆจ่๏ผๆจๅนฟHBNๅ็ฝๆฐดไนณใ
ๅ
ๅฎน้ฟๅบฆ๏ผ่พ้ฟ"""
Use example (with template)
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "ContaAI/ContaLLM-Beauty-8B-Instruct"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
system_prompt = '่ฏทๆ นๆฎ็จๆทๆไพ็่ฅ้้ๆฑๅๅ
ถไปไฟกๆฏๅไธ็ฏ็พๅฆๆค่ค่กไธ็่ฅ้ๆจๆใ'
user_prompt = """่ฅ้้ๆฑ๏ผ็พ็ฝๆฐดไนณๆจ่๏ผๆจๅนฟHBNๅ็ฝๆฐดไนณใ
ๅ
ณ้ฎ่ฏ๏ผHBNๅ็ฝๆฐดไนณ
่ฏ้ข๏ผ ๅไบซๆค่ค ๆไบฎ่ค่ฒ
ๆ ็ญพ๏ผ็ฑๆ
ใๆตชๆผซ
่ฏ้ข๏ผ ๅไบซๆค่ค ๆไบฎ่ค่ฒ
ไบบ่ฎพ๏ผ็พ็ฝๆฐดไนณๆจ่๏ผๆจๅนฟHBNๅ็ฝๆฐดไนณใ
็ธๅ
ณ็ด ๆ๏ผ็พ็ฝๆฐดไนณๆจ่๏ผๆจๅนฟHBNๅ็ฝๆฐดไนณใ
ๅ
ๅฎน้ฟๅบฆ๏ผ่พ้ฟ"""
prompt_template = '''<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{}<|eot_id|><|start_header_id|>user<|end_header_id|>
{}<|eot_id|><|start_header_id|>assistant<|end_header_id|>'''
prompt = prompt_template.format(system_prompt, user_prompt)
tokenized_message = tokenizer(
prompt,
max_length=2048,
return_tensors="pt",
add_special_tokens=False
)
response_token_ids= model.generate(
**tokenized_message,
max_new_tokens=1024,
do_sample=True,
top_p=1.0,
temperature=0.5,
min_length=None,
use_cache=True,
top_k=50,
repetition_penalty=1.2,
length_penalty=1,
)
generated_tokens = response_token_ids[0, tokenized_message['input_ids'].shape[-1]:]
generated_text = tokenizer.decode(generated_tokens, skip_special_tokens=True)
print(generated_text)
Bias, Risks, and Limitations
The ContaLLM models implemented safety techniques during data generation and training, but they are not deployed automatically with in-the-loop filtering of responses like ChatGPT during inference, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base Llama 3.1 models, however it is likely to have included a mix of Web data and technical sources like books and code. The use of the models is at your own risk. You may need to monitor the outputs of the model and take appropriate actions such as content filtering if necessary.
License and use
All Llama 3.1 ContaAI models are released under Meta's Llama 3.1 Community License Agreement.
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