BigDL
alpaca.cpp
| BigDL | alpaca.cpp | |
|---|---|---|
| 5 | 94 | |
| 6,051 | 9,878 | |
| 17.1% | - | |
| 9.9 | 9.4 | |
| 4 days ago | about 1 year ago | |
| Python | C | |
| Apache License 2.0 | MIT License |
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BigDL
- PyTorch Library for Running LLM on Intel CPU and GPU
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LLaMA Now Goes Faster on CPUs
Any performance benchmark against intel's 'IPEX-LLM'[0] or others?
[0] - https://github.com/intel-analytics/ipex-llm
- BigDL-LLM: running LLM on your laptop using INT4
- Fast, distributed, secure AI for Big Data
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Machine learning on JVM
Intel BigDL for Spark which again is for Spark.
alpaca.cpp
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LLaMA Now Goes Faster on CPUs
Where's the 30B-in-6GB claim? ^FGB in your GH link finds [0] which is neither by jart nor by ggerganov but by another user who promptly gets told to look at [1] where Justine denies that claim.
[0] https://github.com/antimatter15/alpaca.cpp/issues/182 -
Is there potential to short NVDA?
You can just download the language model, dude!!! Everyone doesn’t need to make their own and the open source models literally get better every day.
- [Oobabooga] Alpaca.cpp est extrêmement simple à travailler.
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Hollywood’s Screenwriters Are Right to Fear AI
Alpaca
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Square Enix’s AI Tech Demo Is a Staggering Failure
Square could have also trained a more specific data source for their NLP, very similar to Alpaca. Alpaca was trained from interactions from a larger dataset. So while it isn't as smart, it's still able to understand instructions and act upon them.
- [Singularity] Ich bin Alpaka 13B - Frag mich alles
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Alpaca Vs. Final Jeopardy
The model I found was in 8 parts. The alpaca.cpp chat client (chat.cpp) needs to be modified to run the 8 part model, documented here: https://github.com/antimatter15/alpaca.cpp/issues/149
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LocalAI: OpenAI compatible API to run LLM models locally on consumer grade hardware!
try the instructions on this github repo https://github.com/antimatter15/alpaca.cpp, its not the best one but I was able to run this model on my linux machine with 16GB memory, I think its a good starting point.
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What educational materials do you think would be most useful during/after collapse?
Doesn't run offline. If you're running something without a beefy-ish GPU, there's https://github.com/antimatter15/alpaca.cpp .
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ChatGPT Reignited My Passion For Coding
Ye, atm. toying with alpaca 7B/13B in a local install.
What are some alternatives?
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
gpt4all - gpt4all: run open-source LLMs anywhere
Zeppelin - Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more.
llama.cpp - LLM inference in C/C++
Axle - Axle Domain Specific Language for Scientific Cloud Computing and Visualization
coral-pi-rest-server - Perform inferencing of tensorflow-lite models on an RPi with acceleration from Coral USB stick
deequ - Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
ggml - Tensor library for machine learning
Spire - Powerful new number types and numeric abstractions for Scala.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
PredictionIO - PredictionIO, a machine learning server for developers and ML engineers.
alpaca-lora - Instruct-tune LLaMA on consumer hardware