--- license: apache-2.0 language: - en library_name: peft --- ## Usage Here is an example of how you would load: ```python import torch from peft import AutoPeftModelForCausalLM from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("mwz/zephyr-khaadi") inputs = tokenizer(inp_str, return_tensors="pt").to("cuda") model = AutoPeftModelForCausalLM.from_pretrained( "mwz/zephyr-khaadi", low_cpu_mem_usage=True, return_dict=True, torch_dtype=torch.float16, device_map="cuda") generation_config = GenerationConfig( do_sample=True, top_k=1, temperature=0.1, max_new_tokens=150, pad_token_id=tokenizer.eos_token_id ) def process_data_sample(messages): processed_example = "" for message in messages: role = message["role"] content = message["content"] processed_example += f"<|"+role+"|>\n "+content+"\n" return processed_example ``` Inference can then be performed as usual with HF models as follows: ```python messages = [ {"role": "system", "content": "You are a Khaadi Social Media Post Generator who helps with user queries or generate him khaadi posts give only three hashtags and be concise as possible dont try to make up."}, {"role": "user", "content": "Generate post on new arrival of winter"}, ] inp_str = process_data_sample(messages) inputs = tokenizer(inp_str, return_tensors="pt").to("cuda") outputs = model.generate(**inputs, generation_config=generation_config) asnwer = tokenizer.decode(outputs[0], skip_special_tokens=True) print(asnwer) ``` Expected output similar to the following: ``` <|system|> You are a Khaadi Social Media Post Generator who helps with user queries or generate him khaadi posts give only three hashtags and be concise as possible dont try to make up. <|user|> Generate post on new arrival of winter #Khaadi #WinterArrivals #Winter21 Winter is here and we’ve got you covered! Available in-stores and online #Khaadi #WinterCollection #Winter2024 #WinterArrivals #Khaadi #KhaadiFabrics #KhaadiHome ```