tiny-world-map
llama3
tiny-world-map | llama3 | |
---|---|---|
3 | 21 | |
1,355 | 22,014 | |
20.5% | 21.7% | |
9.1 | 9.0 | |
about 1 month ago | 10 days ago | |
HTML | Python | |
ODC Open Database License v1.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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tiny-world-map
- FLaNK AI-April 22, 2024
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Tiny World Map
edit: OP apparently [fixed it](https://github.com/tinyworldmap/tiny-world-map/issues/6)
6 places now, so it's [accurate to 11cm](
llama3
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How Meta trains large language models at scale
and deceptive if not inaccurate. Meta's Model Cards specifically call out that they were trained on publicly available datasets and NOT any Meta user data.
For example: https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md
- Reproduce GPT-2 (124M) in llm.c in 90 minutes for $20
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Hugging Face is sharing $10M worth of compute to help beat the big AI companies
I was curious so I tried to answer this question
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Training Llama 3 models emitted 2290 tons CO2e (https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md), and took 7.7 million GPU hours. Those GPU hours are for H100s, which consume 700W. So the conversion is approximately 2290 / (7.7e6 * 3600 * 700 / 1e9) ~= 0.12 tons CO2e per GPU-gigajoule.
A100s (what Huggingface offers) consume 400W (https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Cent...) and cost $2.21/hour (in e.g. CoreWeave https://www.coreweave.com/gpu-cloud-pricing). So $10 million in H100s buys you ($10e6 / $2.21/h * 3600s/h) * 400W ~= 6515 Gigajoules in GPU-hours.
So Huggingface's offering will emit ~781 tons CO2e. Less if they've inflated the value of the compute they provide, which they have an incentive to do, but let's round to 800 tons.
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According to https://www.carbonindependent.org/22.html, one Boeing-737-400 flying for 926km emits (3.61 tons fuel/flight * 3.15(g CO2e / g fuel)) = 11.37 tons CO2e .
So $10million in compute is like ~72 Boeing-737-400 international flights.
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International Scientific Report on the Safety of Advanced AI [pdf]
> It takes years to become competent at the math needed for AI
(Assuming that "AI" refers to large language models)
The best open source LLM fits in less than 300 lines of code and consists mostly of matrix multiplications. https://github.com/meta-llama/llama3/blob/main/llama/model.p...
Anyone with a basic grasp of linear algebra can probably learn to understand it in a week.
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Llama3.np: pure NumPy implementation of Llama3
From the readme [0]:
> All models support sequence length up to 8192 tokens, but we pre-allocate the cache according to max_seq_len and max_batch_size values. So set those according to your hardware.
[0] https://github.com/meta-llama/llama3/tree/14aab0428d3ec3a959...
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Hindi-Language AI Chatbot for Enterprises Using Qdrant, MLFlow, and LangChain
Now, let's start building the next part of the chatbot. In this part, we will be using the LLM from Ollama and integrating it with the chatbot. More particularly, we will be using the Llama-3 model. Llama-3 is Meta's latest and most advanced open-source large language model (LLM). It is the successor to the previous Llama 2 model and represents a significant improvement in performance across a variety of benchmarks and tasks. Llama 3 comes in two main versions - an 8 billion parameter model and a 70 billion parameter model. Llama 3 supports longer context lengths of up to 8,000 tokens.
- FLaNK AI-April 22, 2024
- Meta Llama 3 GitHub
- Mark Zuckerberg himself appears in the list of direct contributors to Llama 3
- Mark Zuckerberg: Llama 3, $10B Models, Caesar Augustus, Bioweapons [video]
What are some alternatives?
promptfoo - Test your prompts. Evaluate and compare LLM outputs, catch regressions, and improve prompt quality. [Moved to: https://github.com/promptfoo/promptfoo]
llm - Access large language models from the command-line
text-generation-inference - Large Language Model Text Generation Inference
DeepSeek-Coder - DeepSeek Coder: Let the Code Write Itself
llama - Inference code for Llama models
incubator-xtable - Apache XTable (incubating) is a cross-table converter for lakehouse table formats that facilitates interoperability across data processing systems and query engines.
FLiPStackWeekly - FLaNK AI Weekly covering Apache NiFi, Apache Flink, Apache Kafka, Apache Spark, Apache Iceberg, Apache Ozone, Apache Pulsar, and more...
plandex - AI driven development in your terminal. Designed for large, real-world tasks.
quill - Quill is a modern WYSIWYG editor built for compatibility and extensibility
OpenLogReplicator - Open Source Oracle database CDC
Mage - 🧙 The modern replacement for Airflow. Mage is an open-source data pipeline tool for transforming and integrating data. https://github.com/mage-ai/mage-ai
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.