README.md

---
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-Instruct | 10B | 1T | 49.89 | 28.32 | 38.58 | 54.52 | 71.42 |
| MPT-7B-Chat | 7B | 1T | 36.29 | 26.79 | 8.26 | 51.02 | 75.88 |
| Falcon-7B-Instruct | 7B | 1.5T | 25.21 | 26.34 | 4.93 | 45.82 | 70.61 |
| LLM360-AmberChat | 7B | 1.4T | 36.02 | 27.23 | 6.14 | 43.94 | 73.94 |
| LLaMA2-7B-Chat | 7B | 2T | 47.20 | 28.57 | 23.96 | 53.33 | 78.69 |
| LLaMA2-13B-Chat | 13B | 2T | 53.51 | 28.35 | 37.15 | 59.73 | 82.47 |


## **Citations**
If you use this model in your research, please cite it as follows:
```
@article{}
```