langflow
GPTQ-for-LLaMa
langflow | GPTQ-for-LLaMa | |
---|---|---|
28 | 75 | |
17,467 | 2,916 | |
12.6% | - | |
10.0 | 8.6 | |
5 days ago | 9 months ago | |
JavaScript | Python | |
MIT License | Apache License 2.0 |
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langflow
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News DataStax just bought our startup Langflow
Hey folks I'm the Head of DevRel @ DataStax here and just wanted to share to the HN community that in conjunction with this big acquisition news, the LF team has shipped 1.0-alpha of Langflow.
It's a simple `pip install` and the team would love any and all feedback!
https://github.com/logspace-ai/langflow/
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Node-based AutoGen with local LLMs inside ComfyUI
You can also check langflow, a node UI for langchain https://github.com/logspace-ai/langflow
- Show HN: Rivet – open-source AI Agent dev env with real-world applications
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Using Retrieval Augmented Generation to Clear Our GitHub Backlog
There's a few tools out there like AgentGPT (https://github.com/reworkd/AgentGPT, although it's a more conversational interface), and (https://github.com/logspace-ai/langflow) and others. I think most developers definitely prefer a code-first interface though like a library but haven't found one that's great yet. We've used them in the past but didn't have the best experience so would love to hear if anyone has worked with a library they found really flexible.
- Show HN: ChainForge, a visual tool for prompt engineering and LLM evaluation
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Anyone know how to get LangFlow working with oobabooga?
I found this thread talking about it here: https://github.com/logspace-ai/langflow/issues/263
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Found a fun little open source project called Flowise. It's a drag & drop UI to build your customized LLM flow using LangchainJS
also check https://github.com/logspace-ai/langflow
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What exactly is AutoGPT?
AutoGPT is basically a demo of what you can do with Langchain. If you want to play with Langchain in a drag and drop blueprint environment I suggest Langflow
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Launch HN: Fastgen (YC W23) – Visual Low-Code Back End Builder
Hi, I like this! I'm curious what drove the decision to use the vertical block builder style you chose. I'm partial to node-based editors and have been building things with React Flow recently. LangFlow [1] is a good example, but there's lots of UIs that use a similar interface (e.g. Blender [2] and Unity [3]).
[1] https://github.com/logspace-ai/langflow
[2] https://docs.blender.org/manual/en/3.5/interface/controls/no...
[3] https://unity.com/features/unity-visual-scripting
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Having fun testing CanvasGPT - a new project launching soon
Here's an open source version that's very similar LangFlow
GPTQ-for-LLaMa
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[P] Early in 2023 I put in a lot of work on a new machine learning project. Now I'm not sure what to do with it.
First I want to make it clear this is not a self promotion post. I hope many machine learning people come at me with questions or comments about this project. A little background about myself. I did work on the 4 bits quantization of LLaMA using GPTQ. (https://github.com/qwopqwop200/GPTQ-for-LLaMa). I've been studying AI in-depth for many years now.
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GPT-4 Details Leaked
Deploying the 60B version is a challenge though and you might need to apply 4-bit quantization with something like https://github.com/PanQiWei/AutoGPTQ or https://github.com/qwopqwop200/GPTQ-for-LLaMa . Then you can improve the inference speed by using https://github.com/turboderp/exllama .
If you prefer to use an "instruct" model à la ChatGPT (i.e. that does not need few-shot learning to output good results) you can use something like this: https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored...
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Rambling
I use gptq-for-llama - from this https://github.com/qwopqwop200/GPTQ-for-LLaMa and Pygmalion 7B.
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Now that ExLlama is out with reduced VRAM usage, are there any GPTQ models bigger than 7b which can fit onto an 8GB card?
exllama is an optimized implementation of GPTQ-for-LLaMa, allowing you to run 4-bit quantized language models with GPU at great speeds.
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GGML – AI at the Edge
With a single NVIDIA 3090 and the fastest inference branch of GPTQ-for-LLAMA https://github.com/qwopqwop200/GPTQ-for-LLaMa/tree/fastest-i..., I get a healthy 10-15 tokens per second on the 30B models. IMO GGML is great (And I totally use it) but it's still not as fast as running the models on GPU for now.
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New quantization method AWQ outperforms GPTQ in 4-bit and 3-bit with 1.45x speedup and works with multimodal LLMs
And exactly what Triton version are they comparing against? I just tried the latest version of this, and on my 4090/12900K I get 77 tokens per second for Llama 7B-128g. My own GPTQ CUDA implementation gets 151 tokens/second on the same model, same hardware. That makes it 96% faster, whereas AWQ is only 79% faster. For 30B-128g I'm currently only getting a 110% speedup over Triton compared to their 178%, but it still seems a little disingenuous to compare against their own CUDA implementation only, when they're trying to present the quantization method as being faster for inference.
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Introducing Basaran: self-hosted open-source alternative to the OpenAI text completion API
Thanks for the explanation. I think some repos, like text generation webui used gptq for llama (I don't know if it's this repo or another one), anyway most repo that I saw use external things (like gptq for llama)
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How to use AMD GPU?
cd ../.. git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b triton cd GPTQ-for-LLaMa pip install -r requirements.txt mkdir -p ../text-generation-webui/repositories ln -s ../../GPTQ-for-LLaMa ../text-generation-webui/repositories/GPTQ-for-LLaMa
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Help needed with installing quant_cuda for the WebUI
cd repositories git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa pip install -r requirements.txt
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The installed version of bitsandbytes was compiled without GPU support
# To use the GPTQ models I need to Install GPTQ-for-LLaMa and the monkey patch mkdir repositories cd repositories git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b triton cd GPTQ-for-LLaMa pip install ninja pip install -r requirements.txt cd cd text-generation-webui # download random model python download-model.py xxx/yyy # try to start the gui python server.py # It returns this warning but it runs bin /home/gm/miniconda3/envs/chat/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so /home/gm/miniconda3/envs/chat/lib/python3.10/site-packages/bitsandbytes/cextension.py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable. warn("The installed version of bitsandbytes was compiled without GPU support. " /home/gm/miniconda3/envs/chat/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so: undefined symbol: cadam32bit_grad_fp32
What are some alternatives?
Flowise - Drag & drop UI to build your customized LLM flow
llama.cpp - LLM inference in C/C++
langchain-visualizer - Visualization and debugging tool for LangChain workflows
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
Local-LLM-Comparison-Colab-UI - Compare the performance of different LLM that can be deployed locally on consumer hardware. Run yourself with Colab WebUI.
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
GPTQ-for-LLaMa - 4 bits quantization of LLaMa using GPTQ
serge - A web interface for chatting with Alpaca through llama.cpp. Fully dockerized, with an easy to use API.
stable-diffusion-webui-docker - Easy Docker setup for Stable Diffusion with user-friendly UI