argilla
llama.cpp
argilla | llama.cpp | |
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15 | 776 | |
3,122 | 57,463 | |
2.3% | - | |
9.8 | 10.0 | |
5 days ago | 4 days ago | |
Python | C++ | |
Apache License 2.0 | MIT License |
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argilla
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Open-Source Data Collection Platform for LLM Fine-Tuning and RLHF
I'm Dani, CEO and co-founder of Argilla.
Happy to answer any questions you might have and excited to hear your thoughts!
More about Argilla
GitHub: https://github.com/argilla-io/argilla
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Meet Argilla: An Open-Source Data Curation Platform for Large Language Models (LLMs) and MLOps for Natural Language Processing
Github link: https://github.com/argilla-io/argilla
- Show HN: Argilla and AutoTrain – Train custom NLP models without code
- Rubrix release 0.17.0 with support for the spaCy training format
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No training data, no problem! Few-shot NER with a practical example
Rubrix, the open-source tool for data-centric NLP: https://github.com/recognai/rubrix
- [D] Expert Advice is needed on designing a feedback Loop for a (Textual Classification + NER) task in Production.
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[D] How should a former Web Developer, pursue career in Machine Learning?
E.g. https://github.com/recognai/rubrix
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[P] Small-Text: Active Learning for Text Classification in Python
I have already thought about providing an example of how to integrate small-text with one of the existing labeling tools, such as rubrix rubrix, but that hasn't been started yet.
- Finding and correcting text classification label errors with cleanlab and Rubrix | https://rubrix.readthedocs.io/en/master/tutorials/find_label_errors.html
- Rubrix: Open-source tool for building NLP training sets (now with weak supervision)
llama.cpp
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IBM Granite: A Family of Open Foundation Models for Code Intelligence
if you can compile stuff, then looking at llama.cpp (what ollama uses) is also interesting: https://github.com/ggerganov/llama.cpp
the server is here: https://github.com/ggerganov/llama.cpp/tree/master/examples/...
And you can search for any GGUF on huggingface
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Ask HN: Affordable hardware for running local large language models?
Yes, Metal seems to allow a maximum of 1/2 of the RAM for one process, and 3/4 of the RAM allocated to the GPU overall. There’s a kernel hack to fix it, but that comes with the usual system integrity caveats. https://github.com/ggerganov/llama.cpp/discussions/2182
- Xmake: A modern C/C++ build tool
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Better and Faster Large Language Models via Multi-Token Prediction
For anyone interested in exploring this, llama.cpp has an example implementation here:
https://github.com/ggerganov/llama.cpp/tree/master/examples/...
- Llama.cpp Bfloat16 Support
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Fine-tune your first large language model (LLM) with LoRA, llama.cpp, and KitOps in 5 easy steps
Getting started with LLMs can be intimidating. In this tutorial we will show you how to fine-tune a large language model using LoRA, facilitated by tools like llama.cpp and KitOps.
- GGML Flash Attention support merged into llama.cpp
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Phi-3 Weights Released
well https://github.com/ggerganov/llama.cpp/issues/6849
- Lossless Acceleration of LLM via Adaptive N-Gram Parallel Decoding
- Llama.cpp Working on Support for Llama3
What are some alternatives?
snorkel - A system for quickly generating training data with weak supervision
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
gpt4all - gpt4all: run open-source LLMs anywhere
doccano - Open source annotation tool for machine learning practitioners.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
data-centric-ai - Resources for Data Centric AI
ggml - Tensor library for machine learning
trankit - Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM