Understanding_the_EM_Algorithm
StableLM
| Understanding_the_EM_Algorithm | StableLM | |
|---|---|---|
| 1 | 43 | |
| 7 | 15,852 | |
| - | 0.2% | |
| 0.0 | 5.0 | |
| about 2 years ago | 26 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
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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.
Understanding_the_EM_Algorithm
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[D] My new blog post "Understanding the EM Algorithm"
The EM algorithm is very straightforward to understand with one or two proof-of-concept examples. However, if you really want to understand how it works, it may take a while to walk through the math. The purpose of this article is to establish a good intuition for you, while also provide the mathematical proofs for interested readers. The codes for all the examples mentioned in this article can be found at https://github.com/mistylight/Understanding_the_EM_Algorithm.
StableLM
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The Era of 1-bit LLMs: ternary parameters for cost-effective computing
https://github.com/Stability-AI/StableLM?tab=readme-ov-file#...
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Stable LM 3B: Bringing Sustainable, High-Performance LMs to Smart Devices
https://mistral.ai/news/announcing-mistral-7b/
looking at the 3b results (here https://github.com/Stability-AI/StableLM#stablelm-alpha-v2 ?), it looks like Mistral (which outperforms Llama-2 13b) is far more powerful
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FreeWilly 1 and 2, two new open-access LLMs
Does this mean Stability gave up on StableLM?
I notice that the repo hasn’t been updated since April, and a question asking for an update has been ignored for at least a month: https://github.com/Stability-AI/StableLM/issues/83
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In five years, there will be no programmers left, believes Stability AI CEO
I'm not "ignoring" StableLM, if anything it's the impetus for my post. The alpha models were so bad and unusable that it seems they may have simply abandoned the project. It's clear they basically didn't know what they were doing, which is silly for a company of their size and specialization.
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Losing the plot
1) StableLM released a checkpoint at 800B for their 3B and 7B at 800B tokens with 4096 context size, but perform very poorly on different benchmarks and finetuning is discouraged with such a weak base model
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UAE's Technology Innovation Institute Launches Open-Source "Falcon 40B" Large Language Model for Research & Commercial Utilization
It is the best open-source model currently available. Falcon-40B outperforms LLaMA, StableLM, RedPajama, MPT, etc. See the OpenLLM Leaderboard.
- Consulta API GPT
- Google "We Have No Moat, And Neither Does OpenAI"
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New to StableLM--is it possible to use this locally to fine-tune on a small subset of documents yet?
Someone shared this link on another recent post
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[N] Stability AI releases StableVicuna: the world's first open source chatbot trained via RLHF
Github: https://github.com/Stability-AI/StableLM
What are some alternatives?
azureml-examples - Official community-driven Azure Machine Learning examples, tested with GitHub Actions.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
lm-evaluation-harness - A framework for few-shot evaluation of language models.
llama.cpp - LLM inference in C/C++
ggml - Tensor library for machine learning
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
alpaca_lora_4bit
llama - Inference code for Llama models
KoboldAI-Client
lit-llama - Implementation of the LLaMA language model based on nanoGPT. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.
stanford_alpaca - Code and documentation to train Stanford's Alpaca models, and generate the data.