Model Card for Model ID

This repository contains a LLaMA-13B further fine-tuned model on conversations and question answering prompts.

⚠️ I used LLaMA-13B-hf as a base model, so this model is for Research purpose only (See the license)

Model Details

Anyone can use (ask prompts) and play with the model using the pre-existing Jupyter Notebook in the noteboooks folder. The Jupyter Notebook contains example code to load the model and ask prompts to it as well as example prompts to get you started.

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:

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.

You can git clone the repo, which contains also the artifacts for the base model for simplicity and completeness, and run the following code snippet to load the mode:

import torch
from peft import PeftConfig, PeftModel
from transformers import GenerationConfig, LlamaTokenizer, LlamaForCausalLM

MODEL_NAME = "Sandiago21/llama-13b-hf-prompt-answering"

config = PeftConfig.from_pretrained(MODEL_NAME)

# Setting the path to look at your repo directory, assuming that you are at that directory when running this script
config.base_model_name_or_path = "decapoda-research/llama-13b-hf/"

model = LlamaForCausalLM.from_pretrained(
    config.base_model_name_or_path,
    load_in_8bit=True,
    torch_dtype=torch.float16,
    device_map="auto",
)

tokenizer = LlamaTokenizer.from_pretrained(MODEL_NAME)

model = PeftModel.from_pretrained(model, MODEL_NAME)

generation_config = GenerationConfig(
    temperature=0.2,
    top_p=0.75,
    top_k=40,
    num_beams=4,
    max_new_tokens=32,
)

model.eval()
if torch.__version__ >= "2":
    model = torch.compile(model)

Example of Usage

instruction = "What is the capital city of Greece and with which countries does Greece border?"
input_ctxt = None  # For some tasks, you can provide an input context to help the model generate a better response.

prompt = generate_prompt(instruction, input_ctxt)
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
input_ids = input_ids.to(model.device)

with torch.no_grad():
    outputs = model.generate(
        input_ids=input_ids,
        generation_config=generation_config,
        return_dict_in_generate=True,
        output_scores=True,
    )

response = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)
print(response)

>>> The capital city of Greece is Athens and it borders Turkey, Bulgaria, Macedonia, Albania, and the Aegean Sea.

You can directly call the model from HuggingFace using the following code snippet:

import torch
from peft import PeftConfig, PeftModel
from transformers import GenerationConfig, LlamaTokenizer, LlamaForCausalLM

MODEL_NAME = "Sandiago21/llama-13b-hf-prompt-answering"
BASE_MODEL = "decapoda-research/llama-13b-hf"

config = PeftConfig.from_pretrained(MODEL_NAME)

model = LlamaForCausalLM.from_pretrained(
    BASE_MODEL,
    load_in_8bit=True,
    torch_dtype=torch.float16,
    device_map="auto",
)

tokenizer = LlamaTokenizer.from_pretrained(MODEL_NAME)

model = PeftModel.from_pretrained(model, MODEL_NAME)

generation_config = GenerationConfig(
    temperature=0.2,
    top_p=0.75,
    top_k=40,
    num_beams=4,
    max_new_tokens=32,
)

model.eval()
if torch.__version__ >= "2":
    model = torch.compile(model)

Example of Usage

instruction = "What is the capital city of Greece and with which countries does Greece border?"
input_ctxt = None  # For some tasks, you can provide an input context to help the model generate a better response.

prompt = generate_prompt(instruction, input_ctxt)
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
input_ids = input_ids.to(model.device)

with torch.no_grad():
    outputs = model.generate(
        input_ids=input_ids,
        generation_config=generation_config,
        return_dict_in_generate=True,
        output_scores=True,
    )

response = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)
print(response)

>>> The capital city of Greece is Athens and it borders Turkey, Bulgaria, Macedonia, Albania, and the Aegean Sea.

Training Details

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
num_epochs: 2
mixed_precision_training: Native AMP

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu117
Datasets 2.12.0
Tokenizers 0.12.1

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.