--- license: llama3.1 language: - zh pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 - ContaLLM - ContaAI base_model: - meta-llama/Llama-3.1-8B-Instruct library_name: transformers --- ContaLLM # 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](https://huggingface.co/ContaAI/ContaLLM-Beauty-8B-Instruct) | | **Beauty** | **8bit** | [ContaAI/ContaLLM-Beauty-8B-Instruct-8bit](https://huggingface.co/ContaAI/ContaLLM-Beauty-8B-Instruct-8bit) | | **Beauty** | **4bit** | [ContaAI/ContaLLM-Beauty-8B-Instruct-4bit](https://huggingface.co/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-8bit") ``` ### System Prompt The model is a Chinese beauty marketing model, so we use this system prompt by default: ``` system_prompt = '请根据用户提供的营销需求和其他信息写一篇美妆护肤行业的营销推文。' ``` ### 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 | |-------------------|-----------------------|------------------------------------------------------------------------------------------------------| | **营销需求** | required | Fill in your marketing requirements, cannot be blank | | **关键词** | optional | Fill in your marketing keywords, or remove this row from the prompt | | **话题** | optional | Fill in your marketing topic, or remove this row from the prompt | | **标签** | optional | Fill in the hashtag, or remove this row from the prompt | | **营销节点** | optional | Fill in the marketing season, such as Valentine's Day, Christmas, or remove this row from the prompt | | **人设** | optional | Fill in your character settings, or remove this row from the prompt | | **相关素材** | optional | Fill in the relevant materials for your marketing needs, or remove this row from the prompt | | **内容长度** | optional | choices=['较长', '中等', '较短'], choose what you need, or remove this row from the prompt | Example: ``` user_prompt = '营销需求：美白水乳推荐，推广HBN原白水乳。\n关键词：HBN原白水乳\n话题： 分享护肤 提亮肤色\n标签：爱情、浪漫\n话题： 分享护肤 提亮肤色\n人设：美白水乳推荐，推广HBN原白水乳。\n相关素材：美白水乳推荐，推广HBN原白水乳。\n内容长度：较长\n' ``` ### Use example (with template) ``` import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "ContaAI/ContaLLM-Beauty-8B-Instruct-8bit" model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto") tokenizer = AutoTokenizer.from_pretrained(model_name) system_prompt = '请根据用户提供的营销需求和其他信息写一篇美妆护肤行业的营销推文。' user_prompt = '营销需求：美白水乳推荐，推广HBN原白水乳。\n关键词：HBN原白水乳\n话题： 分享护肤 提亮肤色\n标签：爱情、浪漫\n话题： 分享护肤 提亮肤色\n人设：美白水乳推荐，推广HBN原白水乳。\n相关素材：美白水乳推荐，推广HBN原白水乳。\n内容长度：较长\n' 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](https://www.llama.com/llama3_1/license/).