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maddes8cht/
VMware-open-llama-7b-v2-open-instruct-gguf

Name: VMware-open-llama-7b-v2-open-instruct-gguf
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⚠️ ARCHIVED / LEGACY MODEL NOTICE
This repository is part of a legacy collection quantized around 2023. To manage storage quotas and maintain active community projects, some rarely used quantization formats (e.g., Q2_K, Q3_K, Q4_1, Q5_1) have been permanently removed.

Only the most popular and stable formats (Q4_0, Q4_K_M, Q5_K_M, Q6_K, and Q8_0) remain available.

💡 Looking for something modern? If you are starting a new project, we highly recommend using newer architectures (like Llama 3, Mistral, or Qwen) provided by official maintainers or active community members (e.g., Bartowski, TheBloke legacy files, or official organization handles).

⚠️ This repository is no longer actively maintained. Existing files are provided "as is" for archival and legacy hardware purposes.

I'm constantly enhancing these model descriptions to provide you with the most relevant and comprehensive information

open-llama-7b-v2-open-instruct - GGUF

Model creator: VMware
Original model: open-llama-7b-v2-open-instruct

OpenLlama is a free reimplementation of the original Llama Model which is licensed under Apache 2 license.

About GGUF format

gguf is the current file format used by the ggml library. A growing list of Software is using it and can therefore use this model. The core project making use of the ggml library is the llama.cpp project by Georgi Gerganov

Quantization variants

There is a bunch of quantized files available to cater to your specific needs. Here's how to choose the best option for you:

Legacy quants

Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are legacy quantization types. Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants.

Note:

Now there's a new option to use K-quants even for previously 'incompatible' models, although this involves some fallback solution that makes them not real K-quants. More details can be found in affected model descriptions. (This mainly refers to Falcon 7b and Starcoder models)

K-quants

K-quants are designed with the idea that different levels of quantization in specific parts of the model can optimize performance, file size, and memory load. So, if possible, use K-quants. With a Q6_K, you'll likely find it challenging to discern a quality difference from the original model - ask your model two times the same question and you may encounter bigger quality differences.

Original Model Card:

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) 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

Evaluation

TODO

End of original Model File

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