StableLM
AlpacaDataCleaned
| StableLM | AlpacaDataCleaned | |
|---|---|---|
| 43 | 14 | |
| 15,853 | 1,394 | |
| 0.2% | - | |
| 5.0 | 7.6 | |
| about 1 month ago | about 1 year ago | |
| Jupyter Notebook | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
StableLM
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The Era of 1-bit LLMs: ternary parameters for cost-effective computing
https://github.com/Stability-AI/StableLM?tab=readme-ov-file#...
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Stable LM 3B: Bringing Sustainable, High-Performance LMs to Smart Devices
https://mistral.ai/news/announcing-mistral-7b/
looking at the 3b results (here https://github.com/Stability-AI/StableLM#stablelm-alpha-v2 ?), it looks like Mistral (which outperforms Llama-2 13b) is far more powerful
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FreeWilly 1 and 2, two new open-access LLMs
Does this mean Stability gave up on StableLM?
I notice that the repo hasn’t been updated since April, and a question asking for an update has been ignored for at least a month: https://github.com/Stability-AI/StableLM/issues/83
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In five years, there will be no programmers left, believes Stability AI CEO
I'm not "ignoring" StableLM, if anything it's the impetus for my post. The alpha models were so bad and unusable that it seems they may have simply abandoned the project. It's clear they basically didn't know what they were doing, which is silly for a company of their size and specialization.
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Losing the plot
1) StableLM released a checkpoint at 800B for their 3B and 7B at 800B tokens with 4096 context size, but perform very poorly on different benchmarks and finetuning is discouraged with such a weak base model
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UAE's Technology Innovation Institute Launches Open-Source "Falcon 40B" Large Language Model for Research & Commercial Utilization
It is the best open-source model currently available. Falcon-40B outperforms LLaMA, StableLM, RedPajama, MPT, etc. See the OpenLLM Leaderboard.
- Consulta API GPT
- Google "We Have No Moat, And Neither Does OpenAI"
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New to StableLM--is it possible to use this locally to fine-tune on a small subset of documents yet?
Someone shared this link on another recent post
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[N] Stability AI releases StableVicuna: the world's first open source chatbot trained via RLHF
Github: https://github.com/Stability-AI/StableLM
AlpacaDataCleaned
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While training LoRA I get 'Failed to read file... JSON parse error'
I tried using the default alpaca_data_cleaned.json training dataset as mentioned here: https://github.com/gururise/AlpacaDataCleaned/blob/main/alpaca_data_cleaned.json. Does anyone know why I could be getting this error? The file must be in correct format since it is the default file they have shown in their example.
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Why run LLMs locally?
This cleaned alpaca dataset gives a good idea of how data is formatted for the standard alpaca json format. Personally, I'd handle making your own datasets by using gpt4 to format the data into a dataset. You can do it by hand or use a llama model, but I've personally just found using chatgpt to be the most efficient way to get the highest possible output. I'm trying to go for quality over quantity.
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New llama LoRA trained on WizardLM dataset
I created a dataset merge based on the following very high quality datasets:
- [P] Finetuning a commercially viable open source LLM (Flan-UL2) using Alpaca, Dolly15K and LoRA
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Stability AI Launches the First of Its StableLM Suite of Language Models
That dataset is licensed under CC BY NC 4.0, which is not open. It also has a bunch of garbage in it; see https://github.com/gururise/AlpacaDataCleaned
- Alpacino-13B
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GPT4-X-Alpaca 30B 4-bit, by MetaIX based on LoRA by chansung
The alpaca cleaned dataset has integrated the Microsoft GPT-4 dataset and cleaned many of the issues.
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Alpaca, LLaMa, Vicuna [D]
13b Alpaca Cleaned (trained on the cleaned dataset) is very impressive and works well as an instruct model w/o any censorship.
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Is there a good place to post datasets for the community?
There's already a community maintained Alpaca with cleaned data. https://github.com/gururise/AlpacaDataCleaned And a huge amount of work has already been done.
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Dirty data sets and LLaMA/ALPACA...
this might be what you're looking for: https://github.com/gururise/AlpacaDataCleaned
What are some alternatives?
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
safetensors - Simple, safe way to store and distribute tensors
lm-evaluation-harness - A framework for few-shot evaluation of language models.
koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI
llama.cpp - LLM inference in C/C++
simpleAI - An easy way to host your own AI API and expose alternative models, while being compatible with "open" AI clients.
ggml - Tensor library for machine learning
GPT-4-LLM - Instruction Tuning with GPT-4
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
txtinstruct - 📚 Datasets and models for instruction-tuning
alpaca_lora_4bit
ue5-llama-lora - A proof-of-concept project that showcases the potential for using small, locally trainable LLMs to create next-generation documentation tools.