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jamesthesnake merged 4 commits into from
Sep 25, 2023
Merged

here to update #172

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74aa7d9
initial commit: add colossal llama 2 (#4784)
TongLi3701 Sep 24, 2023
ce77785
[feature] ColossalEval: Evaluation Pipeline for LLMs (#4786)
chengeharrison Sep 24, 2023
d512a4d
[doc] add llama2 domain-specific solution news (#4789)
binmakeswell Sep 25, 2023
26cd6d8
[fix] fix weekly runing example (#4787)
flybird11111 Sep 25, 2023
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34 changes: 32 additions & 2 deletions README.md
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</div>

## Latest News
* [2023/09] [One Half-Day of Training Using a Few Hundred Dollars Yields Similar Results to Mainstream Large Models, Open-Source and Commercial-Free Domain-Specific Llm Solution](https://www.hpc-ai.tech/blog/one-half-day-of-training-using-a-few-hundred-dollars-yields-similar-results-to-mainstream-large-models-open-source-and-commercial-free-domain-specific-llm-solution)
* [2023/09] [70 Billion Parameter LLaMA2 Model Training Accelerated by 195%](https://www.hpc-ai.tech/blog/70b-llama2-training)
* [2023/07] [HPC-AI Tech Raises 22 Million USD in Series A Funding](https://www.hpc-ai.tech/blog/hpc-ai-tech-raises-22-million-usd-in-series-a-funding-to-fuel-team-expansion-and-business-growth)
* [2023/07] [65B Model Pretraining Accelerated by 38%, Best Practices for Building LLaMA-Like Base Models Open-Source](https://www.hpc-ai.tech/blog/large-model-pretraining)
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* [2023/03] [AWS and Google Fund Colossal-AI with Startup Cloud Programs](https://www.hpc-ai.tech/blog/aws-and-google-fund-colossal-ai-with-startup-cloud-programs)
* [2023/02] [Open Source Solution Replicates ChatGPT Training Process! Ready to go with only 1.6GB GPU Memory](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt)
* [2023/01] [Hardware Savings Up to 46 Times for AIGC and Automatic Parallelism](https://medium.com/pytorch/latest-colossal-ai-boasts-novel-automatic-parallelism-and-offers-savings-up-to-46x-for-stable-1453b48f3f02)
* [2022/11] [Diffusion Pretraining and Hardware Fine-Tuning Can Be Almost 7X Cheaper](https://www.hpc-ai.tech/blog/diffusion-pretraining-and-hardware-fine-tuning-can-be-almost-7x-cheaper)
* [2022/10] [Use a Laptop to Analyze 90% of Proteins, With a Single-GPU Inference Sequence Exceeding 10,000](https://www.hpc-ai.tech/blog/use-a-laptop-to-analyze-90-of-proteins-with-a-single-gpu-inference-sequence-exceeding)

## Table of Contents
<ul>
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<li>
<a href="#Colossal-AI-in-the-Real-World">Colossal-AI for Real World Applications</a>
<ul>
<li><a href="#Colossal-LLaMA-2">Colossal-LLaMA-2: One Half-Day of Training Using a Few Hundred Dollars Yields Similar Results to Mainstream Large Models, Open-Source and Commercial-Free Domain-Specific Llm Solution</a></li>
<li><a href="#ColossalChat">ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline</a></li>
<li><a href="#AIGC">AIGC: Acceleration of Stable Diffusion</a></li>
<li><a href="#Biomedicine">Biomedicine: Acceleration of AlphaFold Protein Structure</a></li>
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## Colossal-AI in the Real World

### Colossal-LLaMA-2

- One half-day of training using a few hundred dollars yields similar results to mainstream large models, open-source and commercial-free domain-specific LLM solution.
[[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Colossal-LLaMA-2)
[[blog]](https://www.hpc-ai.tech/blog/one-half-day-of-training-using-a-few-hundred-dollars-yields-similar-results-to-mainstream-large-models-open-source-and-commercial-free-domain-specific-llm-solution)
[[model weights]](https://huggingface.co/hpcai-tech/Colossal-LLaMA-2-7b-base)

| | Backbone | Tokens Consumed | | MMLU | CMMLU | AGIEval | GAOKAO | CEval |
| :----------------------------: | :--------: | :-------------: | :------------------: | :-----------: | :-----: | :----: | :----: | :------------------------------: |
| | | - | | 5-shot | 5-shot | 5-shot | 0-shot | 5-shot |
| Baichuan-7B | - | 1.2T | | 42.32 (42.30) | 44.53 (44.02) | 38.72 | 36.74 | 42.80 |
| Baichuan-13B-Base | - | 1.4T | | 50.51 (51.60) | 55.73 (55.30) | 47.20 | 51.41 | 53.60 |
| Baichuan2-7B-Base | - | 2.6T | | 46.97 (54.16) | 57.67 (57.07) | 45.76 | 52.60 | 54.00 |
| Baichuan2-13B-Base | - | 2.6T | | 54.84 (59.17) | 62.62 (61.97) | 52.08 | 58.25 | 58.10 |
| ChatGLM-6B | - | 1.0T | | 39.67 (40.63) | 41.17 (-) | 40.10 | 36.53 | 38.90 |
| ChatGLM2-6B | - | 1.4T | | 44.74 (45.46) | 49.40 (-) | 46.36 | 45.49 | 51.70 |
| InternLM-7B | - | 1.6T | | 46.70 (51.00) | 52.00 (-) | 44.77 | 61.64 | 52.80 |
| Qwen-7B | - | 2.2T | | 54.29 (56.70) | 56.03 (58.80) | 52.47 | 56.42 | 59.60 |
| | | | | | | | | |
| Llama-2-7B | - | 2.0T | | 44.47 (45.30) | 32.97 (-) | 32.60 | 25.46 | - |
| Linly-AI/Chinese-LLaMA-2-7B-hf | Llama-2-7B | 1.0T | | 37.43 | 29.92 | 32.00 | 27.57 | - |
| wenge-research/yayi-7b-llama2 | Llama-2-7B | - | | 38.56 | 31.52 | 30.99 | 25.95 | - |
| ziqingyang/chinese-llama-2-7b | Llama-2-7B | - | | 33.86 | 34.69 | 34.52 | 25.18 | 34.2 |
| TigerResearch/tigerbot-7b-base | Llama-2-7B | 0.3T | | 43.73 | 42.04 | 37.64 | 30.61 | - |
| LinkSoul/Chinese-Llama-2-7b | Llama-2-7B | - | | 48.41 | 38.31 | 38.45 | 27.72 | - |
| FlagAlpha/Atom-7B | Llama-2-7B | 0.1T | | 49.96 | 41.10 | 39.83 | 33.00 | - |
| IDEA-CCNL/Ziya-LLaMA-13B-v1.1 | Llama-13B | 0.11T | | 50.25 | 40.99 | 40.04 | 30.54 | - |
| | | | | | | | | |
| **Colossal-LLaMA-2-7b-base** | Llama-2-7B | **0.0085T** | | 53.06 | 49.89 | 51.48 | 58.82 | 50.2 |

### ColossalChat

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