flash-attention
StableLM
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flash-attention | StableLM | |
---|---|---|
26 | 43 | |
10,773 | 15,853 | |
9.6% | 0.3% | |
9.4 | 5.0 | |
17 days ago | 17 days ago | |
Python | Jupyter Notebook | |
BSD 3-clause "New" or "Revised" License | 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.
flash-attention
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How the Transformer Architecture Was Likely Discovered: A Step-by-Step Guide
If you're looking for an implementation, I highly recommend checking out fast attention [https://github.com/Dao-AILab/flash-attention]. It's my go-to, and far better than anything we could whip up here using just PyTorch or TensorFlow.
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Interactive Coloring with ControlNet
* Even if I bought a 3090, I would have to get a computer to go with it, along with a PSU and some cooling. Don't know where to start with that.
[1] https://github.com/Dao-AILab/flash-attention/issues/190
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Coding Self-Attention, Multi-Head Attention, Cross-Attention, Causal-Attention
highly recommend using Tri's implementation https://github.com/Dao-AILab/flash-attention rotary should be built in, and some group overseas even contributed alibi
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PSA: new ExLlamaV2 quant method makes 70Bs perform much better at low bpw quants
Doesn't seem so https://github.com/Dao-AILab/flash-attention/issues/542 No updates for a while.
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VLLM: 24x faster LLM serving than HuggingFace Transformers
I wonder how this compares to Flash Attention (https://github.com/HazyResearch/flash-attention), which is the other "memory aware" Attention project I'm aware of.
I guess Flash Attention is more about utilizing memory GPU SRam correctly, where this is more about using the OS/CPU memory better?
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Hacking Around ChatGPT’s Character Limits with the Code Interpreter
https://github.com/HazyResearch/flash-attention
- Flash Attention on Consumer
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Unlimiformer: Long-Range Transformers with Unlimited Length Input
After a very quick read, that's my understanding too: It's just KNN search. So I agree on points 1-3. When something works well, I don't care much about point 4.
I've had only mixed success with KNN search. Maybe I haven't done it right? Nothing seems to work quite as well for me as explicit token-token interactions by some form of attention, which as we all know is too costly for long sequences (O(n²)). Lately I've been playing with https://github.com/hazyresearch/safari , which uses a lot less compute and seems promising. Otherwise, for long sequences I've yet to find something better than https://github.com/HazyResearch/flash-attention for n×n interactions and https://github.com/glassroom/heinsen_routing for n×m interactions. If anyone here has other suggestions, I'd love to hear about them.
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Ask HN: Bypassing GPT-4 8k tokens limit
Longer sequence length in transformers is an active area of research (see e.g the great work from the Flash-attention team - https://github.com/HazyResearch/flash-attention), and I'm sure will improve things dramatically very soon.
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Scaling Transformer to 1M tokens and beyond with RMT
Here's a list of tools for scaling up transformer context that have github repos:
* FlashAttention: In my experience, the current best solution for n² attention, but it's very hard to scale it beyond the low tens of thousands of tokens. Code: https://github.com/HazyResearch/flash-attention
* Heinsen Routing: In my experience, the current best solution for n×m attention. I've used it to pull up more than a million tokens as context. It's not a substitute for n² attention. Code: https://github.com/glassroom/heinsen_routing
* RWKV: A sort-of-recurrent model which claims to have performance comparable to n² attention in transformers. In my limited experience, it doesn't. Others agree: https://twitter.com/arankomatsuzaki/status/16390003799784038... . Code: https://github.com/BlinkDL/RWKV-LM
* RMT (this method): I'm skeptical that the recurrent connections will work as well as n² attention in practice, but I'm going to give it a try. Code: https://github.com/booydar/t5-experiments/tree/scaling-repor...
In addition, there's a group at Stanford working on state-space models that looks promising to me. The idea is to approximate n² attention dynamically using only O(n log n) compute. There's no code available, but here's a blog post about it: https://hazyresearch.stanford.edu/blog/2023-03-27-long-learn...
If anyone here has other suggestions for working with long sequences (hundreds of thousands to millions of tokens), I'd love to learn about them.
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
What are some alternatives?
xformers - Hackable and optimized Transformers building blocks, supporting a composable construction.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
lm-evaluation-harness - A framework for few-shot evaluation of language models.
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
llama.cpp - LLM inference in C/C++
memory-efficient-attention-pytorch - Implementation of a memory efficient multi-head attention as proposed in the paper, "Self-attention Does Not Need O(n²) Memory"
ggml - Tensor library for machine learning
RWKV-LM - RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
alpaca_lora_4bit