--- license: other inference: false --- **Sailor 1.8B AWQ** - Model creator: Sea AI Lab - Original model: Sailor 1.8B Sailor is a suite of Open Language Models tailored for South-East Asia (SEA), focusing on languages such as 🇮🇩Indonesian, 🇹🇭Thai, 🇻🇳Vietnamese, 🇲🇾Malay, and 🇱🇦Lao. Developed with careful data curation, Sailor models are designed to understand and generate text across diverse linguistic landscapes of SEA region. Built from Qwen 1.5 , Sailor encompasses models of varying sizes, spanning from 0.5B to 7B versions for different requirements. We further fine-tune the base model with open-source datasets to get instruction-tuned models, namedly Sailor-Chat. Benchmarking results demonstrate Sailor's proficiency in tasks such as question answering, commonsense reasoning, and other tasks in SEA languages. **Description** This repo contain AWQ format model files for Sailor Sailor 1.8B. **Prompt Format** ``` prompt_template = "{prompt}" ``` **Quickstart** Here provides a code snippet to show you how to load the tokenizer and model and how to generate contents. - Using transformers ``` from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Matheusuz/Sailor-1.8B-AWQ" # Model model = AutoModelForCausalLM.from_pretrained( model_name, low_cpu_mem_usage=True, device_map="cuda:0" ) # Tokenizer tokenizer = AutoTokenizer.from_pretrained(model_name) # Prompt template prompt_template = "Artificial intelligence is" # Convert prompt to tokens tokens = tokenizer( prompt_template, return_tensors='pt' ).input_ids.cuda() # Model parameters generation_params = { "do_sample": True, "temperature": 0.7, "top_p": 0.95, "top_k": 40, "max_new_tokens": 512, "repetition_penalty": 1.1 } # Generation generation_output = model.generate( tokens, **generation_params ) # Get the tokens from the output, decode them, print them token_output = generation_output[0] text_output = tokenizer.decode(token_output) print(text_output) ``` **License** Sailor is distributed under the terms of the Qwen License.