metadata

license: cc-by-sa-3.0
datasets:
  - VMware/open-instruct-v1-oasst-dolly-hhrlhf
language:
  - en
library_name: transformers
pipeline_tag: text-generation

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

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

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

Instruction-tuned version of the fully trained Open LLama 7B model. The model is open for COMMERCIAL USE.

There is a v2 version of this model available, https://huggingface.co/VMware/open-llama-7b-v2-open-instruct

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

Commercially Viable
Instruction dataset, VMware/open-instruct-v1-oasst-dolly-hhrlhf is under cc-by-sa-3.0
Language Model, (openlm-research/open_llama_7b) is under apache-2.0

Nomenclature

Model : Open-llama
Model Size: 7B parameters
Dataset: Open-instruct-v1 (oasst,dolly, hhrlhf)

Use in Transformers

import os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = 'VMware/open-llama-7b-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 = 'Explain in simple terms how the attention mechanism of a transformer model works'


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)

'''
 Attention is a mechanism used in deep learning models, such as transformer models, to capture global dependencies between different parts of the input. In a transformer model, the attention mechanism works by computing a weighted sum of the input vectors and then applying a non-linear activation function to the result.

The attention mechanism in a transformer model works in two steps:

1. Query-Key Mapping: First, the input sequence is divided into two parts: the query vector and the key vector. The query vector represents the input at the current position, and the key vector represents the input at a previous position.

2. Attention Weight Calculation: Second, the attention weights are calculated using the dot product between the query vector and each key vector. The attention weights represent the importance of the input at the previous position to the current position.

The attention weights are then used to compute the attention score for each input element. The attention score represents the relevance of the input element to the current position.

The attention mechanism in a transformer model is designed to capture global dependencies between different parts of the input. By attending to input elements from different positions, the model can learn to understand the relationships between different parts of the input. This allows the model to perform more complex tasks, such as understanding the relationships between words in a sentence or pixels in an image.</s>

'''

Finetuning details

The finetuning scripts will be available in our RAIL Github Repository

Evaluation

TODO

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	40.9
ARC (25-shot)	49.74
HellaSwag (10-shot)	73.67
MMLU (5-shot)	31.52
TruthfulQA (0-shot)	34.65
Winogrande (5-shot)	65.43
GSM8K (5-shot)	0.53
DROP (3-shot)	30.75

End of original Model File

Please consider to support my work

Coming Soon: I'm in the process of launching a sponsorship/crowdfunding campaign for my work. I'm evaluating Kickstarter, Patreon, or the new GitHub Sponsors platform, and I am hoping for some support and contribution to the continued availability of these kind of models. Your support will enable me to provide even more valuable resources and maintain the models you rely on. Your patience and ongoing support are greatly appreciated as I work to make this page an even more valuable resource for the community.