Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- PrimeIntellect/fineweb-edu
+- PrimeIntellect/fineweb
+- PrimeIntellect/StackV1-popular
+- mlfoundations/dclm-baseline-1.0-parquet
+- open-web-math/open-web-math
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# INTELLECT-1-step-17000
+
+This is an intermediate checkpoint of INTELLECT-1. You can find the [final version](https://huggingface.co/PrimeIntellect/INTELLECT-1) as well as the [instruct one](https://huggingface.co/PrimeIntellect/INTELLECT-1-Instruct)
+
+|  | Step | Model URL |
+|---|------|-----------|
+| -> | 17000 | https://huggingface.co/PrimeIntellect/INTELLECT-1-step-17000 |
+| | 28600 | https://huggingface.co/PrimeIntellect/INTELLECT-1-step-28600 |
+| | 39200 | https://huggingface.co/PrimeIntellect/INTELLECT-1-step-39200 |
+| | 49200 | https://huggingface.co/PrimeIntellect/INTELLECT-1-step-49200 |
+| | 59200 | https://huggingface.co/PrimeIntellect/INTELLECT-1-step-59200 |
+| | 69200 | https://huggingface.co/PrimeIntellect/INTELLECT-1-step-69200 |
+| | 78000 | https://huggingface.co/PrimeIntellect/INTELLECT-1-step-78000 |
+| | 88000 | https://huggingface.co/PrimeIntellect/INTELLECT-1-step-88000 |
+
+## **Model Overview**
+**INTELLECT-1** is the first collaboratively trained 10 billion parameter language model trained from scratch on 1 trillion tokens of English text and code.
+
+**INTELLECT-1** was trained on up to 14 concurrent nodes distributed across 3 continents, with contributions from 30 independent community contributors providing compute.
+The training code utilizes the [prime framework](https://github.com/PrimeIntellect-ai/prime), a scalable distributed training framework designed for fault-tolerant, dynamically scaling, high-perfomance training on unreliable, globally distributed workers.
+The key abstraction that allows dynamic scaling is the `ElasticDeviceMesh` which manages dynamic global process groups for fault-tolerant communication across the internet and local process groups for communication within a node
+The global all-reduce was done with custom int8 all-reduce kernels to reduce the communication payload required, greatly reducing the communication overhead.
+
+For more detailed technical insights, please refer to our [technical paper](https://github.com/PrimeIntellect-ai/prime).
+
+## **Model Details**
+- **Model Contributors**: samsja, Prime Intellect, Arcee AI, kotaro, skre_0, marlo, rodeo, Herb, Olas, superchillen, Hugging Face, mev_pete, 0xfr_, dj, primeprimeint1234, Marco Giglio, realtek, Hyperbolic, hecataeus, NWO, Virtual Machine, droll, SemiAnalysis, _waiting__, toptickcrypto, sto, Johannes, washout_segment_0b, klee
+- **Release Date**: 29 Nov 2024
+- **Model License**: Apache 2.0
+
+## **Technical Specifications**
+| **Parameter**       | **Value**              |
+|----------------------|------------------------|
+| Parameter Size       | 10B |
+| Number of Layers     | 42 |
+| Number of Attention Heads | 32 |
+| Hidden Size          | 4096 |
+| Context Length       | 8192 |
+| Vocabulary Size      | 128256 |
+
+## **Citations**
+If you use this model in your research, please cite it as follows:
+```
+@article{}
+```