So you don't have to click the link, here's the full text including links:

>Some of my favourite @huggingface models I've quantized in the last week (as always, original models are linked in my repo so you can check out any recent changes or documentation!): > >@shishirpatil_ gave us gorilla's openfunctions-v2, a great followup to their initial models: https://huggingface.co/bartowski/gorilla-openfunctions-v2-exl2 > >@fanqiwan released FuseLLM-VaRM, a fusion of 3 architectures and scales: https://huggingface.co/bartowski/FuseChat-7B-VaRM-exl2 > >@IBM used a new method called LAB (Large-scale Alignment for chatBots) for our first interesting 13B tune in awhile: https://huggingface.co/bartowski/labradorite-13b-exl2 > >@NeuralNovel released several, but I'm a sucker for DPO models, and this one uses their Neural-DPO dataset: https://huggingface.co/bartowski/Senzu-7B-v0.1-DPO-exl2 > >Locutusque, who has been making the Hercules dataset, released a preview of "Hyperion": https://huggingface.co/bartowski/hyperion-medium-preview-exl2 > >@AjinkyaBawase gave an update to his coding models with code-290k based on deepseek 6.7: https://huggingface.co/bartowski/Code-290k-6.7B-Instruct-exl2 > >@Weyaxi followed up on the success of Einstein v3 with, you guessed it, v4: https://huggingface.co/bartowski/Einstein-v4-7B-exl2 > >@WenhuChen with TIGER lab released StructLM in 3 sizes for structured knowledge grounding tasks: https://huggingface.co/bartowski/StructLM-7B-exl2 > >and that's just the highlights from this past week! If you'd like to see your model quantized and I haven't noticed it somehow, feel free to reach out :)

> PolyMind is a multimodal, function calling powered LLM webui. It's designed to be used with Mixtral 8x7B + TabbyAPI and offers a wide range of features including:

> Internet searching with DuckDuckGo and web scraping capabilities. > > Image generation using comfyui. > > Image input with sharegpt4v (Over llama.cpp's server)/moondream on CPU, OCR, and Yolo. > > Port scanning with nmap. > > Wolfram Alpha integration. > > A Python interpreter. > > RAG with semantic search for PDF and miscellaneous text files. > > Plugin system to easily add extra functions that are able to be called by the model. 90% of the web parts (HTML, JS, CSS, and Flask) are written entirely by Mixtral.

Open source

Open data

Open training code

Fully reproducible and auditable

Pretty interesting stuff for embeddings, I'm going to try it for my RAG pipeline when I get a chance, I've not had as much success as I was hoping, maybe this english-focused one will help

Thanks to Charles for the conversion scripts, I've converted several of the new internLM2 models into Llama format. I've also made them into ExLlamaV2 while I was at it.

You can find them here:

https://huggingface.co/bartowski?search_models=internlm2

Note, the chat models seem to do something odd without outputting [UNUSED_TOKEN_145] in a way that seems equivalent to <|im_end|>, not sure why, but it works fine despite outputting that at the end.

Based off of deepseek coder, the current SOTA 33B model, allegedly has gpt 3.5 levels of performance, will be excited to test once I've made exllamav2 quants and will try to update with my findings as a copilot model

Paper abstract: > Recent work demonstrates that, after being fine-tuned on a high-quality instruction dataset, the resulting model can obtain impressive capabilities to address a wide range of tasks. However, existing methods for instruction data generation often produce duplicate data and are not controllable enough on data quality. In this paper, we extend the generalization of instruction tuning by classifying the instruction data to 4 code-related tasks and propose a LLM-based Generator-Discriminator data process framework to generate diverse, high-quality instruction data from open source code. Hence, we introduce CodeOcean, a dataset comprising 20,000 instruction instances across 4 universal code-related tasks,which is aimed at augmenting the effectiveness of instruction tuning and improving the generalization ability of fine-tuned model. Subsequently, we present WaveCoder, a fine-tuned Code LLM with Widespread And Versatile Enhanced instruction tuning. This model is specifically designed for enhancing instruction tuning of Code Language Models (LLMs). Our experiments demonstrate that Wavecoder models outperform other open-source models in terms of generalization ability across different code-related tasks at the same level of fine-tuning scale. Moreover, Wavecoder exhibits high efficiency in previous code generation tasks. This paper thus offers a significant contribution to the field of instruction data generation and fine-tuning models, providing new insights and tools for enhancing performance in code-related tasks.

Available in instruct only currently:

https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2

Early speculation is that it's an MoE (mixture of experts) of 8 7b models, so maybe not earth shattering like their last release but highly intriguing, will update with more info as it comes out

Orca 2 released by Microsoft!

Full weights here:

https://huggingface.co/microsoft/Orca-2-7b

https://huggingface.co/microsoft/Orca-2-13b

My own exllamav2 quants here:

https://huggingface.co/bartowski/Orca-2-7b-exl2

https://huggingface.co/bartowski/Orca-2-13b-exl2

GGUF from TheBloke (links to GPTQ/AWQ inside it):

https://huggingface.co/TheBloke/Orca-2-7B-GGUF

https://huggingface.co/TheBloke/Orca-2-13B-GGUF

LMSYS examines how improper data decontamination can lead to artificially inflated scores

H200 is up to 1.9x faster than H100. This performance is enabled by H200's larger, faster HBM3e memory.

https://nvidianews.nvidia.com/news/nvidia-supercharges-hopper-the-worlds-leading-ai-computing-platform

The creator of ExLlamaV2 (turboderp) has released a lightweight web UI for running exllamav2, it's quite nice! Missing some stuff from text-generation-webui, but makes up for it by being very streamlined and clean

I've made a docker image for it for anyone who may want to try it out, GitHub repo here:

https://github.com/noneabove1182/exui-docker

And for finding models to run with exllamav2 I've been uploading several here:

https://huggingface.co/bartowski

Enjoy!

Phind is now using a V7 of their model for their own platform, as they have found that people overall prefer that output vs GPT4. This is extremely impressive because it's not just a random benchmark that can be gamed, but instead crowd sourced opinion on real tasks

The one place everything still lags behind GPT4 is question comprehension, but this is a huge accomplishment

Blog post: https://www.phind.com/blog/phind-model-beats-gpt4-fast

note: they've only open released V2 of their model, hopefully they release newer versions soon.. would love to play with them outside their sandbox

Very interesting new sampler, does a better drop at filtering out extremely unlikely tokens when the most likely tokens are less confident, from the results it seems to pretty reliably improve quality with no noticeable downside

30T tokens, 20.5T in English, allegedly high quality, can't wait to see people start putting it to use!

Related github: https://github.com/togethercomputer/RedPajama-Data

Finally got a nice script going that automates most of the process. Uploads will all be same format, with each bit per weight going into its own branch.

the first two I did don't have great READMEs but the rest will look like this one: https://huggingface.co/bartowski/Mistral-7B-claude-chat-exl2

Also taking recommendations on anything you want to see included in readme or quant levels

For anyone who happens to be using my docker images or Dockerfiles for their text-gen-webui, it all started breaking this week when Oobabooga's work was updated to support 12.1

As such, I have updated my docker images and fixed a bunch of issues in the build process. Also been awhile since I posted it here.

You can find all the details here:

https://github.com/noneabove1182/text-generation-webui-docker

It requires driver version 535.113.01

Happy LLMing!

From the tweet (minus pictures):

Language models are bad a basic math.

GPT-4 has right around 0% accuracy rate on 5 digit multiplication.

Most open models can't even add. Why is that?

There are a few reasons why numbers are hard. The main one is Tokenization. When training a tokenizer from scratch, you take a large corpus of text and find the minimal byte-pair encoding for a chosen vocabulary size.

This means, however, that numbers will almost certainly not have unique token representations. "21" could be a single token, or ["2", "1"]. 143 could be ["143"] or ["14", "3"] or any other combination.

A potential fix here would be to force single digit tokenization. The state of the art for the last few years is to inject a space between every digit when creating the tokenizer and when running the model. This means 143 would always be tokenized as ["1", "4", "3"].

This helps boost performance, but wastes tokens while not fully fixing the problem.

A cool fix might be xVal! This work by The Polymathic AI Collaboration suggests a generic [NUM] token which is then scaled by the actual value of the number!

If you look at the red lines in the image above, you can get an intuition for how that might work.

It doesn't capture a huge range or high fidelity (e.g., 7.4449 vs 7.4448) but they showcase some pretty convincing results on sequence prediction problems that are primarily numeric.

For example, they want to train a sequence model on GPS conditioned temperature forecasting

They found a ~70x improvement over standard vanilla baselines and a 2x improvement over really strong baselines.

One cool side effect is that deep neural networks might be really good at regression problems using this encoding scheme!