Row 6269

Row ID: 6269 | Dataset Entry | Axioma AXP Content Repository

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Hey all! We are here to share our latest work: consistency large language models (CLLMs), which is a new family of models capable of reducing inference latency by efficiently decoding 𝑛 tokens in parallel. Your new friends for LLM serving/local deployment with faster inference speed! 🔥 Please check our blog post for demo with 3.1x speedup:

[https://hao-ai-lab.github.io/blogs/cllm/](https://hao-ai-lab.github.io/blogs/cllm/)

Compared with existing fast decoding techniques, CLLMs achieve fast parallel decoding **without the need for**:

* Draft models * Architectural modifications/auxiliary model components

This introduces a number of advantages for CLLMs:

* CLLMs don't have to deal with the complexity of obtaining 'good' draft models and managing two different models in a single system. * CLLMs share the same architecture with target LLMs and require no additional engineering efforts when adopting the technique to different models. * CLLMs can be integrated seamlessly with other techniques for efficient LLM inference (e.g. Lookahead Decoding) to achieve even more significant speedup.

This decoding method CLLMs use is called [Jacobi decoding](https://arxiv.org/abs/2305.10427), which improves inference efficiency in comparison with conventional auto-regressive decoding. CLLMs are trained with the objective of performing efficient Jacobi decoding by mapping any randomly initialized 𝑛-token sequence to the same result as AR decoding in as few steps as possible.

Experiment results have demonstrated the effectiveness of CLLMs, showing 2.4× to 3.4× improvements in generation speed on a variety of tasks.

[In comparison with Medusa2, CLLMs achieve comparable or better performance, but \*\*need no extra parameters or tree-style verification\*\*](https://preview.redd.it/fta39wapq9zc1.png?width=640&format=png&auto=webp&s=af39917a635c57a3d8886f8db3a952c0716909c9)

[CLLMs training objective visualization](https://preview.redd.it/nb7pulpqq9zc1.png?width=2560&format=png&auto=webp&s=d4b1ac36e92050579350bdae1e5225a9a5dc6387)

Please see [our paper](http://arxiv.org/abs/2403.00835) for more details. Feel free to try out [our codebase](https://github.com/hao-ai-lab/Consistency_LLM) and [CLLM checkpoints](https://huggingface.co/cllm)!

If you found our work interesting, please subscribe, like or repost, thanks! Learn more and engage with us on Twitter:

[https://x.com/haoailab/status/1788269848788869299](https://x.com/haoailab/status/1788269848788869299)

Field	Value
text	Hey all! We are here to share our latest work: consistency large language models (CLLMs), which is a new family of models capable of reducing inference latency by efficiently decoding 𝑛 tokens in parallel. Your new friends for LLM serving/local deployment with faster inference speed! 🔥 Please check our blog post for demo with 3.1x speedup: [https://hao-ai-lab.github.io/blogs/cllm/](https://hao-ai-lab.github.io/blogs/cllm/) Compared with existing fast decoding techniques, CLLMs achieve fast p…
label	r/machinelearning
dataType	post
communityName	r/MachineLearning
datetime	2024-05-08
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Raw Record

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  "text": "Hey all! We are here to share our latest work: consistency large language models (CLLMs), which is a new family of models capable of reducing inference latency by efficiently decoding 𝑛 tokens in parallel. Your new friends for LLM serving/local deployment with faster inference speed! 🔥 Please check our blog post for demo with 3.1x speedup:\n\n[https://hao-ai-lab.github.io/blogs/cllm/](https://hao-ai-lab.github.io/blogs/cllm/)\n\nCompared with existing fast decoding techniques, CLLMs achieve fast parallel decoding **without the need for**:\n\n* Draft models\n* Architectural modifications/auxiliary model components\n\nThis introduces a number of advantages for CLLMs:\n\n* CLLMs don't have to deal with the complexity of obtaining 'good' draft models and managing two different models in a single system.\n* CLLMs share the same architecture with target LLMs and require no additional engineering efforts when adopting the technique to different models.\n* CLLMs can be integrated seamlessly with other techniques for efficient LLM inference (e.g. Lookahead Decoding) to achieve even more significant speedup.\n\nThis decoding method CLLMs use is called [Jacobi decoding](https://arxiv.org/abs/2305.10427), which improves inference efficiency in comparison with conventional auto-regressive decoding. CLLMs are trained with the objective of performing efficient Jacobi decoding by mapping any randomly initialized 𝑛-token sequence to the same result as AR decoding in as few steps as possible.\n\nExperiment results have demonstrated the effectiveness of CLLMs, showing 2.4× to 3.4× improvements in generation speed on a variety of tasks.\n\n[In comparison with Medusa2, CLLMs achieve comparable or better performance, but \\*\\*need no extra parameters or tree-style verification\\*\\*](https://preview.redd.it/fta39wapq9zc1.png?width=640&format=png&auto=webp&s=af39917a635c57a3d8886f8db3a952c0716909c9)\n\n[CLLMs training objective visualization](https://preview.redd.it/nb7pulpqq9zc1.png?width=2560&format=png&auto=webp&s=d4b1ac36e92050579350bdae1e5225a9a5dc6387)\n\nPlease see [our paper](http://arxiv.org/abs/2403.00835) for more details. Feel free to try out [our codebase](https://github.com/hao-ai-lab/Consistency_LLM) and [CLLM checkpoints](https://huggingface.co/cllm)!\n\nIf you found our work interesting, please subscribe, like or repost, thanks! Learn more and engage with us on Twitter:\n\n[https://x.com/haoailab/status/1788269848788869299](https://x.com/haoailab/status/1788269848788869299)",
  "label": "r/machinelearning",
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  "datetime": "2024-05-08",
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Entry Information

Entry ID: 6269
Repository: Axioma AXP
Dataset: arrmlet/reddit_dataset_36
Total Entries: 100,000