--- license: other language: - en library_name: transformers pipeline_tag: conversational tags: - llama - decapoda-research-13b-hf --- ## Model Card for Model ID Fine-tuned decapoda-research/llama-13b-hf on conversations This repository contains a LLaMA-13B fine-tuned model. ⚠️ **I used [LLaMA-13B-hf](https://huggingface.co/decapoda-research/llama-13b-hf) as a base model, so this model is for Research purpose only (See the [license](https://huggingface.co/decapoda-research/llama-13b-hf/blob/main/LICENSE))** ## Model Details ### Model Description The decapoda-research/llama-13b-hf model was finetuned on conversations and question answering prompts **Developed by:** [More Information Needed] **Shared by:** [More Information Needed] **Model type:** Causal LM **Language(s) (NLP):** English, multilingual **License:** Research **Finetuned from model:** decapoda-research/llama-13b-hf ## Model Sources [optional] **Repository:** [More Information Needed] **Paper:** [More Information Needed] **Demo:** [More Information Needed] ## Uses The model can be used for prompt answering ### Direct Use The model can be used for prompt answering ### Downstream Use Generating text and prompt answering ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. # Usage ## Creating prompt The model was trained on the following kind of prompt: ```python def generate_prompt(instruction: str, input_ctxt: str = None) -> str: if input_ctxt: return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Input: {input_ctxt} ### Response:""" else: return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Response:""" ``` ## How to Get Started with the Model Use the code below to get started with the model. ```python from transformers import LlamaTokenizer, LlamaForCausalLM from peft import PeftModel MODEL_NAME = "decapoda-research/llama-13b-hf" tokenizer = LlamaTokenizer.from_pretrained(MODEL_NAME, add_eos_token=True) tokenizer.pad_token_id = 0 model = LlamaForCausalLM.from_pretrained(MODEL_NAME, load_in_8bit=True, device_map="auto") model = PeftModel.from_pretrained(model, "Sandiago21/public-ai-model") ``` ### Example of Usage ```python from transformers import GenerationConfig PROMPT = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nWhich is the capital city of Greece and with which countries does Greece border?\n\n### Input:\nQuestion answering\n\n### Response:\n""" DEVICE = "cuda" inputs = tokenizer( PROMPT, return_tensors="pt", ) input_ids = inputs["input_ids"].to(DEVICE) generation_config = GenerationConfig( temperature=0.1, top_p=0.95, repetition_penalty=1.2, ) print("Generating Response ... ") generation_output = model.generate( input_ids=input_ids, generation_config=generation_config, return_dict_in_generate=True, output_scores=True, max_new_tokens=256, ) for s in generation_output.sequences: print(tokenizer.decode(s)) ``` ### Example Output ```python Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: Which is the capital city of Greece and with which countries does Greece border? ### Input: Question answering ### Response: Generating... Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: Which is the capital city of Greece and with which countries does Greece border? ### Input: Question answering ### Response: capital city of Athens and it borders Albania to the northwest, North Macedonia and Bulgaria to the northeast, Turkey to the east, and Libya to the southeast across the Mediterranean Sea. ``` ## Training Details ### Training Data The decapoda-research/llama-13b-hf was finetuned on conversations and question answering data ### Training Procedure The decapoda-research/llama-13b-hf model was further trained and finetuned on question answering and prompts data for 1 epoch (approximately 10 hours of training on a single GPU) ## Model Architecture and Objective The model is based on decapoda-research/llama-13b-hf model and finetuned adapters on top of the main model on conversations and question answering data.