convaiinnovations/laya
Text Classification • 0.4B • Updated • 4.38k
bruce hu
bruceunx
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convaiinnovations/laya updated a model about 2 years ago
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reacted to bartowski's post with ❤️ almost 2 years ago
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In regards to the latest mistral model and GGUFs for it:
Yes, they may be subpar and may require changes to llama.cpp to support the interleaved sliding window
Yes, I got excited when a conversion worked and released them ASAP
That said, generation seems to work right now and seems to mimic the output from spaces that are running the original model
I have appended -TEST to the model names in an attempt to indicate that they are not final or perfect, but if people still feel mislead and that it's not the right thing to do, please post (civilly) below your thoughts, I will highly consider pulling the conversions if that's what people think is best. After all, that's what I'm here for, in service to you all !
Yes, they may be subpar and may require changes to llama.cpp to support the interleaved sliding window
Yes, I got excited when a conversion worked and released them ASAP
That said, generation seems to work right now and seems to mimic the output from spaces that are running the original model
I have appended -TEST to the model names in an attempt to indicate that they are not final or perfect, but if people still feel mislead and that it's not the right thing to do, please post (civilly) below your thoughts, I will highly consider pulling the conversions if that's what people think is best. After all, that's what I'm here for, in service to you all !
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updated a
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upvoted an article about 2 years ago
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Introduction to ggml
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ngxson, ggerganov, slaren
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incredible, there are some similar usages before like turn human instructions to robots code in 'Coscientist', maybe this can work better.
reacted to dhuynh95's post with ❤️👍 over 2 years ago
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🌊 Released #LaVague, fullly open-source AI pipeline to turn natural language into browser actions!
In less than 150 lines of code (RAG with local embedding + Zephyr-7b-Gemma locally or Mixtral on HF Inference API), it generates #Selenium code from user query. In this GIF you can see it follow user instructions to command a browser to browse HF website!
Try it on Colab: colab.research.google.com/github/dhuynh95/LaVague/blob/main/LaVague.ipynb
GitHub: github.com/dhuynh95/LaVague
Pretty exciting how it becomes possible to create an AI assistant that could perform actions for us, such as logging on gov accounts, fill forms, or pull personal information!
It was quite fun to hack in the weekend using open-source tools, from @huggingface local embedding with transformers for local inference or HF Inference API, to RAG with @llama_index, through @MistralAI Mixtral model!
Some challenges: to make it run on Colab for the #GPU Poors, I first resorted to @huggingface Inference API with Mixtral as it was the only model good enough (gemma-7b did not make it and refused to produce code). But after some experimentations, I managed to make it work a local Zephyr-7b-Gemma so that people could run this assistant fully locally!
Because I used an off-the-shelf model, I had to improve performance with few-shot learning and Chain Of Thought, which managed to generate appropriate code!
I hope this project will herald a new dawn where transparent, private and local AI assistants help automate menial but critical tasks, such as helping fill taxes, book accomodation, or research information for us.
In less than 150 lines of code (RAG with local embedding + Zephyr-7b-Gemma locally or Mixtral on HF Inference API), it generates #Selenium code from user query. In this GIF you can see it follow user instructions to command a browser to browse HF website!
Try it on Colab: colab.research.google.com/github/dhuynh95/LaVague/blob/main/LaVague.ipynb
GitHub: github.com/dhuynh95/LaVague
Pretty exciting how it becomes possible to create an AI assistant that could perform actions for us, such as logging on gov accounts, fill forms, or pull personal information!
It was quite fun to hack in the weekend using open-source tools, from @huggingface local embedding with transformers for local inference or HF Inference API, to RAG with @llama_index, through @MistralAI Mixtral model!
Some challenges: to make it run on Colab for the #GPU Poors, I first resorted to @huggingface Inference API with Mixtral as it was the only model good enough (gemma-7b did not make it and refused to produce code). But after some experimentations, I managed to make it work a local Zephyr-7b-Gemma so that people could run this assistant fully locally!
Because I used an off-the-shelf model, I had to improve performance with few-shot learning and Chain Of Thought, which managed to generate appropriate code!
I hope this project will herald a new dawn where transparent, private and local AI assistants help automate menial but critical tasks, such as helping fill taxes, book accomodation, or research information for us.
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MobileLLM
Optimizing Sub-billion Parameter Language Models for On-Device Use Cases
MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases (2402.14905)
paper addresses the growing need for efficient large language models (LLMs) on mobile devices, driven by increasing cloud costs and latency concerns. We focus on designing top-quality LLMs with fewer than a billion parameters, a practical choice for mobile deployment. Contrary to prevailing belief emphasizing the pivotal role of data and parameter quantity in determining model quality, our investigation underscores the significance of model architecture for sub-billion scale LLMs. Leveraging deep and thin architectures, coupled with embedding sharing and grouped-query attention mechanisms, we establish a strong baseline network denoted as MobileLLM, which attains a remarkable 2.7%/4.3% accuracy boost over preceding 125M/350M state-of-the-art models. Additionally, we propose an immediate block-wise weight sharing approach with no increase in model size and only marginal latency overhead. The resultant models, denoted as MobileLLM-LS, demonstrate a further accuracy enhancement of 0.7%/0.8% than MobileLLM 125M/350M. Moreover, MobileLLM model family shows significant improvements compared to previous sub-billion models on chat benchmarks, and demonstrates close correctness to LLaMA-v2 7B in API calling tasks, highlighting the capability of small models for common on-device use cases.
Optimizing Sub-billion Parameter Language Models for On-Device Use Cases
MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases (2402.14905)
paper addresses the growing need for efficient large language models (LLMs) on mobile devices, driven by increasing cloud costs and latency concerns. We focus on designing top-quality LLMs with fewer than a billion parameters, a practical choice for mobile deployment. Contrary to prevailing belief emphasizing the pivotal role of data and parameter quantity in determining model quality, our investigation underscores the significance of model architecture for sub-billion scale LLMs. Leveraging deep and thin architectures, coupled with embedding sharing and grouped-query attention mechanisms, we establish a strong baseline network denoted as MobileLLM, which attains a remarkable 2.7%/4.3% accuracy boost over preceding 125M/350M state-of-the-art models. Additionally, we propose an immediate block-wise weight sharing approach with no increase in model size and only marginal latency overhead. The resultant models, denoted as MobileLLM-LS, demonstrate a further accuracy enhancement of 0.7%/0.8% than MobileLLM 125M/350M. Moreover, MobileLLM model family shows significant improvements compared to previous sub-billion models on chat benchmarks, and demonstrates close correctness to LLaMA-v2 7B in API calling tasks, highlighting the capability of small models for common on-device use cases.
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