rllama
stanford_alpaca
rllama | stanford_alpaca | |
---|---|---|
7 | 108 | |
519 | 28,816 | |
- | 0.7% | |
6.2 | 2.0 | |
3 months ago | about 2 months ago | |
Rust | Python | |
GNU Affero General Public License v3.0 | 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.
rllama
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Ask HN: Who wants to be hired? (July 2023)
Location: San Francisco
Remote: No preference, as long as I don't have to move far from Bay Area
Willing to relocate: No
Technologies: C, Rust, Golang, Haskell, Lisp, Python, Lua, OpenGL, SQLite3, JavaScript, PostgreSQL, AWS EC2, S3, ECS, Batch.
Resume: https://www.linkedin.com/in/mikjuola
Email: [email protected]
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I've been working at the Bay Area since 2015, most recently at Pinterest. At work, I've done big data pipelines, designed some batch job systems, computing metrics, handling billing APIs, lots of Python, Go and Java and working with AWS, i.e. backend and data engineer stuff.
But I'm trying to look for work that's more in line with what I do on my free time: Challenging low-level C or Rust programming, machine learning implementations (see e.g. this thing I made https://github.com/Noeda/rllama/, graphics programming or research-type work, uncommon programming languages.
If you scroll through my random crap repositories you can see what kind of things I'm interested in: https://github.com/Noeda?tab=repositories
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State-of-the-art open-source chatbot, Vicuna-13B, just released model weights
No, my project is called rllama. No relation to GGML. https://github.com/Noeda/rllama
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Where can I learn more about SIMD, CPU intrinsics and the like in the context of Rust?
I have seen some Rust attempts as well such as https://github.com/Noeda/rllama/ but they are still way behind the C++ ones. This seems like an interesting space to get into.
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Show HN: Alpaca.cpp – Run an Instruction-Tuned Chat-Style LLM on a MacBook
I ran it on a 128 RAM machine with a Ryzen 5950X. It's not fast, 4 seconds per token. But it's just about fits without swapping. https://github.com/Noeda/rllama/
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Llama.rs – Rust port of llama.cpp for fast LLaMA inference on CPU
I've counted three different Rust LLaMA implementations on r/rust subreddit this week:
https://github.com/Noeda/rllama/ (pure Rust+OpenCL)
https://github.com/setzer22/llama-rs/ (ggml based)
https://github.com/philpax/ggllama (also ggml based)
There's also a discussion on GitHub issue on setzer's repo to collaborate a bit on these separate efforts: https://github.com/setzer22/llama-rs/issues/4
- Rust+OpenCL+AVX2 implementation of LLaMA inference code
- Pure Rust CPU and OpenCL implementation of LLaMA language model
stanford_alpaca
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How Open is Generative AI? Part 2
Alpaca is an instruction-oriented LLM derived from LLaMA, enhanced by Stanford researchers with a dataset of 52,000 examples of following instructions, sourced from OpenAI’s InstructGPT through the self-instruct method. The extensive self-instruct dataset, details of data generation, and the model refinement code were publicly disclosed. This model complies with the licensing requirements of its base model. Due to the utilization of InstructGPT for data generation, it also adheres to OpenAI’s usage terms, which prohibit the creation of models competing with OpenAI. This illustrates how dataset restrictions can indirectly affect the resulting fine-tuned model.
- Ask HN: AI/ML papers to catch up with current state of AI?
- OpenAI board in discussions with Sam Altman to return as CEO
- Are there any AI like ChatGPT without content restrictions?
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Fine-tuning LLMs with LoRA: A Gentle Introduction
In this article, we're going to experiment with LoRA and fine-tune Llama Alpaca using commercial hardware.
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Creating a new Finetuned model
Most papers I did read showed at least a thousand, even 10000 at several cases, so I assumed that to be the trend in the case of Low rank adapter(PEFT) training.(source: [2305.14314] QLoRA: Efficient Finetuning of Quantized LLMs (arxiv.org) , Stanford CRFM (Alpaca) and the minimum being openchat/openchat · Hugging Face ; There are a lot more examples)
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Shock tick up for wage growth to 7.3% in blow for Bank of England
I'm not talking about OpenAI ChatGPT I'm talking about things ALPACA, and where did they train these models? Off the existing models for a fraction of a fraction of a fraction of the cost: https://crfm.stanford.edu/2023/03/13/alpaca.html
- Bye bye Bing
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The idea maze for AI startups (2015)
I think there's a new approach for “How do you get the data?” that wasn't available when this article was written in 2015. The new text and image generative models can now be used to synthesize training datasets.
I was working on an typing autocorrect project and needed a corpus of "text messages". Most of the traditional NLP corpuses like those available through NLTK [0] aren't suitable. But it was easy to script ChatGPT to generate thousands of believable text messages by throwing random topics at it.
Similarly, you can synthesize a training dataset by giving GPT the outputs/labels and asking it to generate a variety of inputs. For sentiment analysis... "Give me 1000 negative movie reviews" and "Now give me 1000 positive movie reviews".
The Alpaca folks used GPT-3 to generate high-quality instruction-following datasets [1] based on a small set of human samples.
Etc.
[0] https://www.nltk.org/nltk_data/
[1] https://crfm.stanford.edu/2023/03/13/alpaca.html
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Repos and tutorials for a full finetune (not LoRA)
AFAIK, the original alpaca repo was a full finetune. https://github.com/tatsu-lab/stanford_alpaca
What are some alternatives?
llama.cpp - LLM inference in C/C++
alpaca-lora - Instruct-tune LLaMA on consumer hardware
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
ChatGLM-6B - ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
ultraviolet - A wide linear algebra crate for games and graphics.
litestar - Production-ready, Light, Flexible and Extensible ASGI API framework | Effortlessly Build Performant APIs
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
80r3d
Alpaca-Turbo - Web UI to run alpaca model locally