--- 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 COMMERCIAL USE.
- 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](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!
## 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) ## Evaluation **TODO**