---
license: other
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- llama
- decapoda-research-7b-hf
- prompt answering
- peft
---

## Model Card for Model ID

This repository contains a LLaMA-7B further fine-tuned model on conversations and question answering prompts.

This model is a fine-tuned version of [chainyo/alpaca-lora-7b](https://huggingface.co/chainyo/alpaca-lora-7b) on conversations dataset.

⚠️ **I used [LLaMA-7b-hf](https://huggingface.co/decapoda-research/llama-7b-hf) as a base model, so this model is for Research purpose only (See the [license](https://huggingface.co/decapoda-research/llama-7b-hf/blob/main/LICENSE))**


## Model Details


### Model Description

The decapoda-research/llama-7b-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-7b-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
import torch
from transformers import GenerationConfig, LlamaTokenizer, LlamaForCausalLM

tokenizer = LlamaTokenizer.from_pretrained("Sandiago21/llama-7b-hf-prompt-answering")
model = LlamaForCausalLM.from_pretrained(
    "Sandiago21/llama-7b-hf-prompt-answering",
    load_in_8bit=True,
    torch_dtype=torch.float16,
    device_map="auto",
)
generation_config = GenerationConfig(
    temperature=0.2,
    top_p=0.75,
    top_k=40,
    num_beams=4,
    max_new_tokens=128,
)

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

### Example of Usage
```python
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 Albania, Macedonia, Bulgaria and Turkey.
```

## 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-7b-hf was finetuned on conversations and question answering data


### Training Procedure

The decapoda-research/llama-7b-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-7b-hf model and finetuned adapters on top of the main model on conversations and question answering data.