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Model: PrimeIntellect/INTELLECT-1-fp32 Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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datasets:
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- PrimeIntellect/fineweb-edu
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- PrimeIntellect/fineweb
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- PrimeIntellect/StackV1-popular
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- mlfoundations/dclm-baseline-1.0-parquet
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- open-web-math/open-web-math
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language:
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- en
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pipeline_tag: text-generation
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---
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# INTELLECT-1
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## **Model Overview**
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**INTELLECT-1** is the first collaboratively trained 10 billion parameter language model trained from scratch on 1 trillion tokens of English text and code.
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**INTELLECT-1** was trained on up to 14 concurrent nodes distributed across 3 continents, with contributions from 30 independent community contributors providing compute.
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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.
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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.
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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.
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For more detailed technical insights, please refer to our [technical paper](https://github.com/PrimeIntellect-ai/prime).
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**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.**
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## Usage
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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torch.set_default_device("cuda")
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model = AutoModelForCausalLM.from_pretrained("PrimeIntellect/INTELLECT-1-fp32")
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tokenizer = AutoTokenizer.from_pretrained("PrimeIntellect/INTELLECT-1-fp32")
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input_text = "What is the Metamorphosis of Prime Intellect about?"
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input_ids = tokenizer.encode(input_text, return_tensors="pt")
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output_ids = model.generate(input_ids, max_length=50, num_return_sequences=1)
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output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
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print(output_text)
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```
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### Example text generation pipeline
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```python
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import torch
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from transformers import pipeline
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torch.set_default_device("cuda")
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pipe = pipeline("text-generation", model="PrimeIntellect/INTELLECT-1")
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print(pipe("What is prime intellect ?"))
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```
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## **Model Details**
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- **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
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- **Release Date**: 29 Nov 2024
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- **Model License**: Apache 2.0
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## **Technical Specifications**
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| **Parameter** | **Value** |
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|----------------------|------------------------|
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| Parameter Size | 10B |
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| Number of Layers | 42 |
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| Number of Attention Heads | 32 |
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| Hidden Size | 4096 |
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| Context Length | 8192 |
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| Vocabulary Size | 128256 |
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**Training Details**:
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- **Dataset**: 55% fineweb-edu, 10% fineweb, 20% Stack V1, 10% dclm-baseline, 5% open-web-math
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- **Tokens**: 1 Trillion
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- **Optimizer**: Diloco/LocalSGD - Inner Optimizer: AdamW, Outer Optmizer: Nesterov SGD
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**Performance on benchmarks**
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| Model | Size | Tokens | MMLU | GPQA | GSM8K | ARC-C | Hellaswag |
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|---|---|---|---|---|---|---|---|
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| INTELLECT-1 | 10B | 1T | 37.5 | 26.12 | 8.1 | 52.13 | 72.26 |
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| LLaMA-7B | 7B | 1T | 35.1 | 23.1 | 9.7 | 50.43 | 78.19 |
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| LLaMA-13B | 13B | 1T | 46.9 | 26.34 | 17.3 | 56.14 | 81.05 |
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| LLaMA2-7B | 7B | 2T | 45.3 | 25.89 | 13.5 | 54.10 | 78.64 |
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| LLaMA2-13B | 13B | 2T | 54.8 | 25.67 | 24.3 | 59.81 | 82.58 |
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| MPT-7B | 7B | 1T | 26.8 | 25.67 | 8.3 | 46.67 | 77.41 |
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| Falcon-7B | 7B | 1.5T | 26.2 | 23.66 | 4.9 | 47.61 | 78.23 |
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| Pythia-12B | 12B | 300B | 26.5 | 24.33 | 4.09 | 40.61 | 68.83 |
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| LLM360-Amber | 7B | 1.3T | 24.5 | 27.01 | 4.3 | 42.75 | 74.08 |
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## **Citations**
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If you use this model in your research, please cite it as follows:
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```
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@article{}
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```
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config.json
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"LlamaForCausalLM"
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 42,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"rope_type": "default"
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},
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"use_cache": true,
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"vocab_size": 128256
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}
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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"do_sample": true,
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"use_cache": false
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}
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||||
16
special_tokens_map.json
Normal file
16
special_tokens_map.json
Normal file
@@ -0,0 +1,16 @@
|
||||
{
|
||||
"bos_token": {
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||||
"content": "<|begin_of_text|>",
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||||
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||||
},
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||||
}
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||||
}
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||||
410563
tokenizer.json
Normal file
410563
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
2061
tokenizer_config.json
Normal file
2061
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user