--- 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 ## **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 training visual](intellect-1-map.png) **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 model was trained using the [DiLoCo](https://arxiv.org/abs/2311.08105) algorithms with 100 inner steps. The global all-reduce was done with custom int8 all-reduce kernels to reduce the communication payload required, greatly reducing the communication overhead by a factor 400x. For more detailed technical insights, please refer to our [technical paper](https://github.com/PrimeIntellect-ai/prime). **Note: The model will immediately output EOS token if the BOS token is not set. This is a result of the tensor packing used during training. This can result in terrible eval scores.** ## Usage ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer torch.set_default_device("cuda") model = AutoModelForCausalLM.from_pretrained("PrimeIntellect/INTELLECT-1-Instruct") tokenizer = AutoTokenizer.from_pretrained("PrimeIntellect/INTELLECT-1-Instruct") input_text = "What is the Metamorphosis of Prime Intellect about?" input_ids = tokenizer.encode(input_text, return_tensors="pt") output_ids = model.generate(input_ids, max_length=50, num_return_sequences=1) output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True) print(output_text) ``` ### Example text generation pipeline ```python import torch from transformers import pipeline torch.set_default_device("cuda") pipe = pipeline("text-generation", model="PrimeIntellect/INTELLECT-1") print(pipe("What is prime intellect ?")) ``` ## **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 | **Training Details**: - **Dataset**: 55% fineweb-edu, 10% fineweb, 20% Stack V1, 10% dclm-baseline, 5% open-web-math - **Tokens**: 1 Trillion - **Optimizer**: Diloco/LocalSGD - Inner Optimizer: AdamW, Outer Optmizer: Nesterov SGD **Performance on benchmarks** | Model | Size | Tokens | MMLU | GPQA | GSM8K | ARC-C | Hellaswag | |---|---|---|---|---|---|---|---| | INTELLECT-1 | 10B | 1T | 37.5 | 26.12 | 8.1 | 52.13 | 72.26 | | LLaMA-7B | 7B | 1T | 35.1 | 23.1 | 9.7 | 50.43 | 78.19 | | LLaMA-13B | 13B | 1T | 46.9 | 26.34 | 17.3 | 56.14 | 81.05 | | LLaMA2-7B | 7B | 2T | 45.3 | 25.89 | 13.5 | 54.10 | 78.64 | | LLaMA2-13B | 13B | 2T | 54.8 | 25.67 | 24.3 | 59.81 | 82.58 | | MPT-7B | 7B | 1T | 26.8 | 25.67 | 8.3 | 46.67 | 77.41 | | Falcon-7B | 7B | 1.5T | 26.2 | 23.66 | 4.9 | 47.61 | 78.23 | | Pythia-12B | 12B | 300B | 26.5 | 24.33 | 4.09 | 40.61 | 68.83 | | LLM360-Amber | 7B | 1.3T | 24.5 | 27.01 | 4.3 | 42.75 | 74.08 | ## **Citations** If you use this model in your research, please cite it as follows: ``` @article{} ```