This model performs better on code compared to v1 due to the improvements made on the base model by the openlm-research team.
The instruction model is trained on an improved instruction tuning dataset compared to v1

NOTE: The model was trained using the Alpaca prompt template
NOTE: Fast tokenizer results in incorrect encoding, set the use_fast = False parameter, when instantiating the tokenizer

License

CC BY-SA-3.0 (Commercially Viable!)
Base Language Model (openlm-research/open_llama_v2_7b) is under apache-2.0
Fine-Tuning Dataset (VMware/open-instruct) is under cc-by-sa-3.0

Datasets used for Fine-Tuning

Open-instruct

Open-instruct-v1

Mosaic/Dolly-HHRLHF + filtered OASST1 - cc by 3.0

Subset of COT SUBMIX (FROM FLAN V2) Zeroshot examples

ESNLI - MIT
ECQA - CDLA 1.0 - Sharing
Strategy - MIT
CREAK - MIT
gsmk8 - MIT
aqua - MIT
qasc - Apache 2.0

Nomenclature

Model : Open-llama-v2
Model Size: 7B parameters
Dataset: Open-instruct

Use in Transformers

import os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = 'VMware/open-llama-7b-v2-open-instruct'


tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)

model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map='sequential')

prompt_template = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"

prompt = """What is attention mechanism of a transformer model? 
 Write a python code to illustrate how attention works within a transformer model using numpy library. Donot use pytorch or tensorflow."""


inputt = prompt_template.format(instruction= prompt)
input_ids = tokenizer(inputt, return_tensors="pt").input_ids.to("cuda")

output1 = model.generate(input_ids, max_length=512)
input_length = input_ids.shape[1]
output1 = output1[:, input_length:]
output = tokenizer.decode(output1[0])

print(output)

Output

Sure, I can help you with that!

Attention mechanisms in transformer models are typically implemented using the attention mechanism in the self-attention layer. Self-attention allows the model to focus on different parts of the input sequence when processing it. This is achieved by computing a set of attention weights, which are used to weigh the contribution of each input element to the output.

Here's an example code using NumPy to illustrate how attention works in a transformer model:

python

import numpy as np

def attention_weights(query, key, value, mask):
    # Query, key, and value are input tensors. Mask is a tensor of zeros and ones that represents the attention mask.
    # It is used to prevent the model from attending to certain positions in the input sequence if they are not relevant.
    # The attention weights are the element-wise product of the query, key, and mask tensors.
    # The result is a tensor of the same shape as the query tensor.
    
    # Compute the dot product between the query tensor and the key tensor
    dot = np.matmul(query, key)
    
    # Compute the element-wise softmax of the dot product tensor
    exp_dot = np.exp(dot)
    
    # Multiply the dot product and the softmax of the dot product tensors
    weights = dot * exp_dot
    
    # Return the attention weights as a NumPy tensor
    return weights

# Define the input sequence
query = np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]])
key = np.array([[0.1, 0.2], [0.3, 0.4]])
value = np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]])
mask = np.array([[False, True, True], [False, True, True]])

# Compute the attention weights
weights = attention_weights(query, key, value, mask)

# Print the attention weights
print(weights)

In this example, the attention_weights function takes as input the query tensor, key tensor, value tensor, and mask tensor. It computes the dot product between the query and key tensors using the np.matmul function, and then applies a softmax function using the np.exp function to the element-wise dot product tensor. It then multiplies the dot product and softmax tensors using the np.matmul function, and returns the result as a NumPy tensor.

The query, key, and value tensors represent the input sequence to the transformer model. The mask tensor represents the attention mask, which is used to prevent the model from attending to certain positions in the input sequence if they are not relevant.

The output of the attention_weights function is a NumPy tensor that represents the attention weights for the input sequence. These weights are used by the transformer model to weigh the contribution of each input element to the output.

I hope this helps!

Finetuning details

The finetuning scripts will be available in our RAIL Github Repository

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	40.34
ARC (25-shot)	39.76
HellaSwag (10-shot)	70.31
MMLU (5-shot)	35.16
TruthfulQA (0-shot)	39.53
Winogrande (5-shot)	64.33
GSM8K (5-shot)	7.43
DROP (3-shot)	25.88