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

gpt4all-falcon - GGUF

Model creator: nomic-ai
Original model: gpt4all-falcon

K-Quants in Falcon 7b models

New Llama.cpp releases now allow for K-quantization of models that were previously incompatible with K-quants. This is achieved by employing a fallback solution for model layers that cannot be accurately quantized with K-quants.

For Falcon 7B models, although only a quarter of the layers can be quantized with true K-quants, this approach still benefits from utilizing various legacy quantization types, such as Q4_0, Q4_1, Q5_0, and Q5_1. As a result, it offers better quality at the same file size or smaller file sizes with comparable performance.

So this solution ensures improved performance and efficiency over legacy Q4_0, Q4_1, Q5_0 and Q5_1 Quantizations.

Important Update for Falcon Models in llama.cpp Versions After October 18, 2023

As previously noted on the Llama.cpp GitHub repository, all new Llama.cpp releases after October 18, 2023, required re-quantization due to the implementation of the new BPE tokenizer.

Update: The re-quantization process for Falcon Models is now complete, and the latest quantized models are available for download. To ensure continued compatibility with recent llama.cpp software, You need to update your Falcon models.

Key Points:

Stay Informed: Keep an eye on software application release schedules using llama.cpp libraries.
Monitor Upload Times: Re-quantization is complete. Watch for updates on my Hugging Face Model pages.

This change primarily affects Falcon and Starcoder models, with other models remaining unaffected. If you haven't already, please update your Falcon models for seamless compatibility with the latest llama.cpp versions.

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:

Model Card for GPT4All-Falcon

An Apache-2 licensed chatbot trained over a massive curated corpus of assistant interactions including word problems, multi-turn dialogue, code, poems, songs, and stories.

Model Details

Model Description

This model has been finetuned from Falcon

Developed by: Nomic AI
Model Type: A finetuned Falcon 7B model on assistant style interaction data
Language(s) (NLP): English
License: Apache-2
Finetuned from model [optional]: Falcon

To download a model with a specific revision run

from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("nomic-ai/gpt4all-falcon", trust_remote_code=True)

Downloading without specifying revision defaults to main/v1.0.

To use it for inference with Cuda, run

from transformers import AutoTokenizer, pipeline
import transformers
import torch

tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
model.to("cuda:0")

prompt = "Describe a painting of a falcon in a very detailed way." # Change this to your prompt
prompt_template = f"### Instruction: {prompt}\n### Response:"

tokens = tokenizer(prompt_template, return_tensors="pt").input_ids.to("cuda:0")
output = model.generate(input_ids=tokens, max_new_tokens=256, do_sample=True, temperature=0.8)

# Print the generated text
print(tokenizer.decode(output[0]))

Model Sources [optional]

Repository: https://github.com/nomic-ai/gpt4all
Base Model Repository: https://huggingface.co/tiiuae/falcon-7b
Demo [optional]: https://gpt4all.io/

Training Procedure

GPT4All is made possible by our compute partner Paperspace.

Trained on a DGX cluster with 8 A100 80GB GPUs for ~12 hours. Using Deepspeed + Accelerate, we use a global batch size of 256 with a learning rate of 2e-5. More information can be found in the repo.

Results

Results on common sense reasoning benchmarks

| Model                     |  BoolQ   |   PIQA   | HellaSwag | WinoGrande |  ARC-e   |  ARC-c   |   OBQA   |   Avg.   |
|:--------------------------|:--------:|:--------:|:---------:|:----------:|:--------:|:--------:|:--------:|:--------:|
| GPT4All-J 6B v1.0         |   73.4   |   74.8   |   63.4    |    64.7    |   54.9   |   36.0   |   40.2   |   58.2   |
| GPT4All-J v1.1-breezy     |   74.0   |   75.1   |   63.2    |    63.6    |   55.4   |   34.9   |   38.4   |   57.8   |
| GPT4All-J v1.2-jazzy      |   74.8   |   74.9   |   63.6    |    63.8    |   56.6   |   35.3   |   41.0   |   58.6   |
| GPT4All-J v1.3-groovy     |   73.6   |   74.3   |   63.8    |    63.5    |   57.7   |   35.0   |   38.8   |   58.1   |
| GPT4All-J Lora 6B         |   68.6   |   75.8   |   66.2    |    63.5    |   56.4   |   35.7   |   40.2   |   58.1   |
| GPT4All LLaMa Lora 7B     |   73.1   |   77.6   |   72.1    |    67.8    |   51.1   |   40.4   |   40.2   |   60.3   |
| GPT4All 13B snoozy        | **83.3** |   79.2   |   75.0    |  **71.3**  |   60.9   |   44.2   |   43.4   |   65.3   |
| GPT4All Falcon            |   77.6   |   79.8   |   74.9    |    70.1    |   67.9   |   43.4   |   42.6   |   65.2   |
| Dolly 6B                  |   68.8   |   77.3   |   67.6    |    63.9    |   62.9   |   38.7   |   41.2   |   60.1   |
| Dolly 12B                 |   56.7   |   75.4   |   71.0    |    62.2    |   64.6   |   38.5   |   40.4   |   58.4   |
| Alpaca 7B                 |   73.9   |   77.2   |   73.9    |    66.1    |   59.8   |   43.3   |   43.4   |   62.4   |
| Alpaca Lora 7B            |   74.3   |   79.3   |   74.0    |    68.8    |   56.6   |   43.9   |   42.6   |   62.8   |
| GPT-J 6.7B                |   65.4   |   76.2   |   66.2    |    64.1    |   62.2   |   36.6   |   38.2   |   58.4   |
| LLama 7B                  |   73.1   |   77.4   |   73.0    |    66.9    |   52.5   |   41.4   |   42.4   |   61.0   |
| LLama 13B                 |   68.5   |   79.1   |   76.2    |    70.1    |   60.0   | **44.6** |   42.2   |   63.0   |
| Pythia 6.7B               |   63.5   |   76.3   |   64.0    |    61.1    |   61.3   |   35.2   |   37.2   |   57.0   |
| Pythia 12B                |   67.7   |   76.6   |   67.3    |    63.8    |   63.9   |   34.8   |    38    |   58.9   |
| Fastchat T5               |   81.5   |   64.6   |   46.3    |    61.8    |   49.3   |   33.3   |   39.4   |   53.7   |
| Fastchat Vicuña 7B        |   76.6   |   77.2   |   70.7    |    67.3    |   53.5   |   41.2   |   40.8   |   61.0   |
| Fastchat Vicuña 13B       |   81.5   |   76.8   |   73.3    |    66.7    |   57.4   |   42.7   |   43.6   |   63.1   |
| StableVicuña RLHF         |   82.3   |   78.6   |   74.1    |    70.9    |   61.0   |   43.5   | **44.4** |   65.0   |
| StableLM Tuned            |   62.5   |   71.2   |   53.6    |    54.8    |   52.4   |   31.1   |   33.4   |   51.3   |
| StableLM Base             |   60.1   |   67.4   |   41.2    |    50.1    |   44.9   |   27.0   |   32.0   |   42.2   |
| Koala 13B                 |   76.5   |   77.9   |   72.6    |    68.8    |   54.3   |   41.0   |   42.8   |   62.0   |
| Open Assistant Pythia 12B |   67.9   |   78.0   |   68.1    |    65.0    |   64.2   |   40.4   |   43.2   |   61.0   |
| Mosaic MPT7B              |   74.8   |   79.3   |   76.3    |    68.6    |   70.0   |   42.2   |   42.6   |   64.8   |
| Mosaic mpt-instruct       |   74.3   |   80.4   | **77.2**  |    67.8    | **72.2** | **44.6** |   43.0   | **65.6** |
| Mosaic mpt-chat           |   77.1   |   78.2   |   74.5    |    67.5    |   69.4   |   43.3   |   44.2   |   64.9   |
| Wizard 7B                 |   78.4   |   77.2   |   69.9    |    66.5    |   56.8   |   40.5   |   42.6   |   61.7   |
| Wizard 7B Uncensored      |   77.7   |   74.2   |   68.0    |    65.2    |   53.5   |   38.7   |   41.6   |   59.8   |
| Wizard 13B Uncensored     |   78.4   |   75.5   |   72.1    |    69.5    |   57.5   |   40.4   |   44.0   |   62.5   |
| GPT4-x-Vicuna-13b         |   81.3   |   75.0   |   75.2    |    65.0    |   58.7   |   43.9   |   43.6   |   62.2   |
| Falcon 7b                 |   73.6   | **80.7** |   76.3    |    67.3    |   71.0   |   43.3   |   44.4   |   65.2   |   
| text-davinci-003          |   88.1   |   83.8   |   83.4    |    75.8    |   83.9   |   63.9   |   51.0   |   75.7   |

End of original Model File

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