README.md

---
license: cc-by-sa-3.0
datasets:
- VMware/open-instruct
language:
- en
library_name: transformers
pipeline_tag: text-generation
---

# VMware/open-llama-7B-v2-open-instruct
Instruction-tuned version of the fully trained Open LLama 7B v2  model. The model is open for <b>COMMERCIAL USE</b>. <br>

- 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 <br>
**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](https://huggingface.co/openlm-research/open_llama_v2_7b)) is under apache-2.0
- Fine-Tuning Dataset ([VMware/open-instruct](https://huggingface.co/datasets/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!</s>
<hr>


## Finetuning details
The finetuning scripts will be available in our [RAIL Github Repository](https://github.com/vmware-labs/research-and-development-artificial-intelligence-lab/tree/main/instruction-tuning)




# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Vmware__open-llama-7b-v2-open-instruct)

| 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         |