---
license: mit
language:
- en
library_name: adapter-transformers
---
# alpaca_orca_open_llama: An Open_LLaMA-3B model trained on Alpaca dataset using Orca Research paper approaches


# Dataset and Training

We train OpenLLaMa-3B model to become more steerable by training it on the custom Alpaca dataset created using approaches from [Orca Research Paper](https://arxiv.org/abs/2306.02707). 

Please pay attention how the **System** prompt is added before each *instruction*.

The training configurations are provided in the table below.

The training takes on 4x A600(50G) GPUs and lasts for around 20 Hours for cost of $66 using [Lambda Labs](https://lambdalabs.com)

We used DeepSpeed with Zero-3 approaches for parallel gpu training.

|||
|:-------------:|:-------------:|
|*batch size*|16|
|*train_micro_batch_size_per_gpu*|2|
|*gradient_accumulation_steps*|2|
|*Learning rate*|2e-5|
|*Epochs*|3|
|*Max length*|1024|



# Example Usage

Below shows an example on how to use OpenAlpaca

```python
import torch
from transformers import LlamaForCausalLM, LlamaTokenizer

# change model_path between 3b,7b or 13b
model_path = 'psmathur/alpaca_orca_open_llama_3b'
tokenizer = LlamaTokenizer.from_pretrained(model_path)
model = LlamaForCausalLM.from_pretrained(
    model_path, torch_dtype=torch.float16, device_map='auto',
)
# check more details here https://github.com/openlm-research/open_llama
tokenizer.bos_token_id, tokenizer.eos_token_id = 1,2

# same prompt as provided by Orca Research Paper
system = 'You are an AI assistant. User will you give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps.'
instruction = 'Use the given data to calculate the median.'
input = '[7, 3, 8, 2, 10]'

prompt_input = f"### System:\n{system}\n\n#\n\n### User:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
#prompt_no_input = f"### System:\n{system}\n\n#\n\n### User:\n{instruction}\n\n### Response:\n"

tokens = tokenizer.encode(prompt_no_input)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to('cuda')

instance = {'input_ids': tokens,'top_k': 50, 'top_p': 1.0, 'generate_len': 1024}
# instance = {'input_ids': tokens,'top_k': 50, 'top_p': 1.0, 'temperature':0.7, 'generate_len': 1024}

with torch.no_grad():
    rest = model.generate(
            input_ids=tokens, 
            max_length=length+instance['generate_len'], 
            use_cache=True, 
            do_sample=True, 
            top_p=instance['top_p'], 
            top_k=instance['top_k'],
            # temperature=instance['temperature']
        )
        
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
print(f'[!] Response: {string}')

```