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open-llama-0.3T-7B-instruct-dolly-hhrlhf - GGUF

Model creator: VMware
Original model: open-llama-0.3T-7B-instruct-dolly-hhrlhf

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-0.3T-7B-instruct-dolly-hhrlhf

Fully Open Source, Commerically viable.

The instruction dataset, mosaicml/dolly_hhrlhf is under cc-by-sa-3.0, and the Language Model (openlm-research/open_llama_7b_preview_300bt) is under apache-2.0 License.

Use in Transformers

Please load the tokenizer with 'add_bos_token = True' parameter as the underlying OpenLLaMa model and this model were trained with a BOS token.

import os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = 'VMware/open-llama-0.3T-7B-instruct-dolly-hhrlhf'


tokenizer = AutoTokenizer.from_pretrained(model_name, add_bos_token = True)

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=  'how do I bake a cake?'


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)

'''
Baking a cake is a simple process. You will need to prepare a cake mixture, then bake it in the oven. You can add various ingredients to the cake mixture, such as fruit, nuts, or spices, to make it flavorful. Baking a cake can be fun, as it creates a delicious dessert!</s>

'''

Drawbacks

The model was trained on a partially trained Open-LLaMA checkpoint. (300B tokens).

Evaluation

TODO

End of original Model File

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