--- library_name: transformers license: apache-2.0 --- # Model Card for Model ID Just only a text gen model type, I'm just test train my dataset and...it's work, very nice, try it. ## Model Details - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1fhVByew053W8SdbacFryIx3luhFkEa1M?usp=sharing) ### Model Description This is the model card of a 🤗 transformers model that has been pushed on the Hub. - **Developed by:** **HuyRemy** - **Funded by :** **HuyRemy** - **Shared by :** **HuyRemy** - **Model type:** **Megatron Mistral** - **License:** huynq@isi.com.vn ### Model Demo: - **Demo :** https://ai.matilda.vn ## Uses **USE T4 GPU** ```Python !pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git" !pip install --no-deps xformers trl peft accelerate bitsandbytes ``` ### Direct Use ``` Python from unsloth import FastLanguageModel import torch max_seq_length = 2048 dtype = None load_in_4bit = True alpaca_prompt = """ ### Instruction: {} ### Input: {} ### Response: {}""" def formatting_prompts_func(examples): instructions = examples["instruction"] inputs = examples["input"] outputs = examples["output"] texts = [] for instruction, input, output in zip(instructions, inputs, outputs): text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN texts.append(text) return { "text" : texts, } pass model, tokenizer = FastLanguageModel.from_pretrained( model_name = "huyremy/aichat", max_seq_length = max_seq_length, dtype = dtype, load_in_4bit = load_in_4bit, ) FastLanguageModel.for_inference(model) EOS_TOKEN = tokenizer.eos_token inputs = tokenizer( [ alpaca_prompt.format( "who is Nguyễn Phú Trọng?", "", "", ), ], return_tensors = "pt").to("cuda") outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True) tokenizer.batch_decode(outputs) ``` ## Model Card Contact huynq@isi.com.vn