SuperEasy Methods To Learn Everything About Deepseek

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Avery McCollister asked 2 weeks ago

DeepSeek also just lately debuted deepseek ai china-R1-Lite-Preview, a language mannequin that wraps in reinforcement studying to get higher efficiency. Reinforcement Learning (RL) Model: Designed to carry out math reasoning with suggestions mechanisms. Import AI runs on lattes, ramen, and suggestions from readers. Jack Clark Import AI publishes first on Substack deepseek ai china makes the very best coding mannequin in its class and releases it as open supply:โ€ฆ Pc, you can too strive the cloud-hosted supply mannequin in Azure Foundry by clicking on the "Try in Playground" button underneath " DeepSeek R1". ๐ŸŒOpen Source! DeepSeek LLM 7B/67B Base&Chat released. It also focuses narrowly on language in its quest to reach AGI reasonably than making an attempt to go multimodal and incorporating photographs, audio and video. ์ด ํšŒ์‚ฌ์˜ ์†Œ๊ฐœ๋ฅผ ๋ณด๋ฉด, โ€˜Making AGI a Realityโ€™, โ€˜Unravel the Mystery of AGI with Curiosityโ€™, โ€˜Answer the Essential Question with Long-termismโ€™๊ณผ ๊ฐ™์€ ํ‘œํ˜„๋“ค์ด ์žˆ๋Š”๋ฐ์š”. DeepSeek ๋ชจ๋ธ์€ ์ฒ˜์Œ 2023๋…„ ํ•˜๋ฐ˜๊ธฐ์— ์ถœ์‹œ๋œ ํ›„์— ๋น ๋ฅด๊ฒŒ AI ์ปค๋ฎค๋‹ˆํ‹ฐ์˜ ๋งŽ์€ ๊ด€์‹ฌ์„ ๋ฐ›์œผ๋ฉด์„œ ์œ ๋ช…์„ธ๋ฅผ ํƒ„ ํŽธ์ด๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋Š”๋ฐ์š”. ์ด์ „ ๋ฒ„์ „์ธ DeepSeek-Coder์˜ ๋ฉ”์ด์ € ์—…๊ทธ๋ ˆ์ด๋“œ ๋ฒ„์ „์ด๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋Š” DeepSeek-Coder-V2๋Š” ์ด์ „ ๋ฒ„์ „ ๋Œ€๋น„ ๋” ๊ด‘๋ฒ”์œ„ํ•œ ํŠธ๋ ˆ์ด๋‹ ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•ด์„œ ํ›ˆ๋ จํ–ˆ๊ณ , โ€˜Fill-In-The-Middleโ€™์ด๋ผ๋“ ๊ฐ€ โ€˜๊ฐ•ํ™”ํ•™์Šตโ€™ ๊ฐ™์€ ๊ธฐ๋ฒ•์„ ๊ฒฐํ•ฉํ•ด์„œ ์‚ฌ์ด์ฆˆ๋Š” ํฌ์ง€๋งŒ ๋†’์€ ํšจ์œจ์„ ๋ณด์—ฌ์ฃผ๊ณ , ์ปจํ…์ŠคํŠธ๋„ ๋” ์ž˜ ๋‹ค๋ฃจ๋Š” ๋ชจ๋ธ์ž…๋‹ˆ๋‹ค.
ํŠนํžˆ, DeepSeek๋งŒ์˜ ๋…์ž์ ์ธ MoE ์•„ํ‚คํ…์ฒ˜, ๊ทธ๋ฆฌ๊ณ  ์–ดํ…์…˜ ๋ฉ”์ปค๋‹ˆ์ฆ˜์˜ ๋ณ€ํ˜• MLA (Multi-Head Latent Attention)๋ฅผ ๊ณ ์•ˆํ•ด์„œ LLM์„ ๋” ๋‹ค์–‘ํ•˜๊ฒŒ, ๋น„์šฉ ํšจ์œจ์ ์ธ ๊ตฌ์กฐ๋กœ ๋งŒ๋“ค์–ด์„œ ์ข‹์€ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ฃผ๋„๋ก ๋งŒ๋“  ์ ์ด ์•„์ฃผ ํฅ๋ฏธ๋กœ์› ์Šต๋‹ˆ๋‹ค. ๋˜ ํ•œ ๊ฐ€์ง€ ์ฃผ๋ชฉํ•  ์ ์€, DeepSeek์˜ ์†Œํ˜• ๋ชจ๋ธ์ด ์ˆ˜๋งŽ์€ ๋Œ€ํ˜• ์–ธ์–ด๋ชจ๋ธ๋ณด๋‹ค ์ƒ๋‹นํžˆ ์ข‹์€ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ค€๋‹ค๋Š” ์ ์ž…๋‹ˆ๋‹ค. 236B ๋ชจ๋ธ์€ 210์–ต ๊ฐœ์˜ ํ™œ์„ฑ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ํฌํ•จํ•˜๋Š” DeepSeek์˜ MoE ๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•ด์„œ, ํฐ ์‚ฌ์ด์ฆˆ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๋ชจ๋ธ์ด ๋น ๋ฅด๊ณ  ํšจ์œจ์ ์ž…๋‹ˆ๋‹ค. So if you think about mixture of specialists, if you look on the Mistral MoE model, which is 8x7 billion parameters, heads, you need about 80 gigabytes of VRAM to run it, which is the biggest H100 out there. The distilled Qwen 1.5B consists of a tokenizer, embedding layer, a context processing model, token iteration mannequin, a language mannequin head and de tokenizer. But perhaps most considerably, buried within the paper is an important insight: you can convert just about any LLM right into a reasoning mannequin in the event you finetune them on the best mix of information - here, 800k samples showing questions and solutions the chains of thought written by the model while answering them. Smaller, specialized fashions trained on high-quality knowledge can outperform larger, basic-purpose models on specific duties.
It specializes in allocating different duties to specialized sub-fashions (specialists), enhancing effectivity and effectiveness in handling various and complex problems. ์ด๋ ‡๊ฒŒ โ€˜์ค€์ˆ˜ํ•œโ€™ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ฃผ๊ธฐ๋Š” ํ–ˆ์ง€๋งŒ, ๋‹ค๋ฅธ ๋ชจ๋ธ๋“ค๊ณผ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ โ€˜์—ฐ์‚ฐ์˜ ํšจ์œจ์„ฑ (Computational Efficiency)โ€™์ด๋ผ๋“ ๊ฐ€โ€™ ํ™•์žฅ์„ฑ (Scalability)โ€™๋ผ๋Š” ์ธก๋ฉด์—์„œ๋Š” ์—ฌ์ „ํžˆ ๋ฌธ์ œ๊ฐ€ ์žˆ์—ˆ์ฃ . ์ด๋ ‡๊ฒŒ ํ•˜๋Š” ๊ณผ์ •์—์„œ, ๋ชจ๋“  ์‹œ์ ์˜ ์€๋‹‰ ์ƒํƒœ๋“ค๊ณผ ๊ทธ๊ฒƒ๋“ค์˜ ๊ณ„์‚ฐ๊ฐ’์„ โ€˜KV ์บ์‹œ (Key-Value Cache)โ€™๋ผ๋Š” ์ด๋ฆ„์œผ๋กœ ์ €์žฅํ•˜๊ฒŒ ๋˜๋Š”๋ฐ, ์ด๊ฒŒ ์•„์ฃผ ๋ฉ”๋ชจ๋ฆฌ๊ฐ€ ๋งŽ์ด ํ•„์š”ํ•˜๊ณ  ๋Š๋ฆฐ ์ž‘์—…์ด์˜ˆ์š”. DeepSeek Coder๋Š” Llama 2์˜ ์•„ํ‚คํ…์ฒ˜๋ฅผ ๊ธฐ๋ณธ์œผ๋กœ ํ•˜์ง€๋งŒ, ํŠธ๋ ˆ์ด๋‹ ๋ฐ์ดํ„ฐ ์ค€๋น„, ํŒŒ๋ผ๋ฏธํ„ฐ ์„ค์ •์„ ํฌํ•จํ•ด์„œ ์ฒ˜์Œ๋ถ€ํ„ฐ ๋ณ„๋„๋กœ ๊ตฌ์ถ•ํ•œ ๋ชจ๋ธ๋กœ, โ€˜์™„์ „ํ•œ ์˜คํ”ˆ์†Œ์Šคโ€™๋กœ์„œ ๋ชจ๋“  ๋ฐฉ์‹์˜ ์ƒ์—…์  ์ด์šฉ๊นŒ์ง€ ๊ฐ€๋Šฅํ•œ ๋ชจ๋ธ์ž…๋‹ˆ๋‹ค. Turning small models into reasoning models: "To equip more environment friendly smaller fashions with reasoning capabilities like DeepSeek-R1, we directly nice-tuned open-source models like Qwen, and Llama utilizing the 800k samples curated with DeepSeek-R1," DeepSeek write. ์ฒ˜์Œ์—๋Š” Llama 2๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๋‹ค์–‘ํ•œ ๋ฒค์น˜๋งˆํฌ์—์„œ ์ฃผ์š” ๋ชจ๋ธ๋“ค์„ ๊ณ ๋ฅด๊ฒŒ ์•ž์„œ๋‚˜๊ฐ€๊ฒ ๋‹ค๋Š” ๋ชฉํ‘œ๋กœ ๋ชจ๋ธ์„ ๊ฐœ๋ฐœ, ๊ฐœ์„ ํ•˜๊ธฐ ์‹œ์ž‘ํ–ˆ์Šต๋‹ˆ๋‹ค. ๋ถˆ๊ณผ ๋‘ ๋‹ฌ ๋งŒ์—, DeepSeek๋Š” ๋ญ”๊ฐ€ ์ƒˆ๋กญ๊ณ  ํฅ๋ฏธ๋กœ์šด ๊ฒƒ์„ ๋“ค๊ณ  ๋‚˜์˜ค๊ฒŒ ๋ฉ๋‹ˆ๋‹ค: ๋ฐ”๋กœ 2024๋…„ 1์›”, ๊ณ ๋„ํ™”๋œ MoE (Mixture-of-Experts) ์•„ํ‚คํ…์ฒ˜๋ฅผ ์•ž์„ธ์šด DeepSeekMoE์™€, ์ƒˆ๋กœ์šด ๋ฒ„์ „์˜ ์ฝ”๋”ฉ ๋ชจ๋ธ์ธ DeepSeek-Coder-v1.5 ๋“ฑ ๋”์šฑ ๋ฐœ์ „๋˜์—ˆ์„ ๋ฟ ์•„๋‹ˆ๋ผ ๋งค์šฐ ํšจ์œจ์ ์ธ ๋ชจ๋ธ์„ ๊ฐœ๋ฐœ, ๊ณต๊ฐœํ•œ ๊ฒ๋‹ˆ๋‹ค.
์ž, ์ด๋ ‡๊ฒŒ ์ฐฝ์—…ํ•œ์ง€ ๊ฒจ์šฐ ๋ฐ˜๋…„ ๋‚จ์ง“ํ•œ ๊ธฐ๊ฐ„๋™์•ˆ ์Šคํƒ€ํŠธ์—… DeepSeek๊ฐ€ ์ˆจ๊ฐ€์˜๊ฒŒ ๋‹ฌ๋ ค์˜จ ๋ชจ๋ธ ๊ฐœ๋ฐœ, ์ถœ์‹œ, ๊ฐœ์„ ์˜ ์—ญ์‚ฌ(?)๋ฅผ ํ์–ด๋ดค๋Š”๋ฐ์š”. ์ž, ์ด์ œ DeepSeek-V2์˜ ์žฅ์ , ๊ทธ๋ฆฌ๊ณ  ๋‚จ์•„์žˆ๋Š” ํ•œ๊ณ„๋“ค์„ ์•Œ์•„๋ณด์ฃ . ์ž, ๊ทธ๋ฆฌ๊ณ  2024๋…„ 8์›”, ๋ฐ”๋กœ ๋ฉฐ์น  ์ „ ๊ฐ€์žฅ ๋”ฐ๋ˆ๋”ฐ๋ˆํ•œ ์‹ ์ƒ ๋ชจ๋ธ์ด ์ถœ์‹œ๋˜์—ˆ๋Š”๋ฐ์š”. ๊ธฐ์กด์˜ MoE ์•„ํ‚คํ…์ฒ˜๋Š” ๊ฒŒ์ดํŒ… ๋ฉ”์ปค๋‹ˆ์ฆ˜ (Sparse Gating)์„ ์‚ฌ์šฉํ•ด์„œ ๊ฐ๊ฐ์˜ ์ž…๋ ฅ์— ๊ฐ€์žฅ ๊ด€๋ จ์„ฑ์ด ๋†’์€ ์ „๋ฌธ๊ฐ€ ๋ชจ๋ธ์„ ์„ ํƒํ•˜๋Š” ๋ฐฉ์‹์œผ๋กœ ์—ฌ๋Ÿฌ ์ „๋ฌธ๊ฐ€ ๋ชจ๋ธ ๊ฐ„์— ์ž‘์—…์„ ๋ถ„ํ• ํ•ฉ๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๊ฐ๊ฐ์˜ ์ „๋ฌธ๊ฐ€๊ฐ€ ์ž๊ธฐ๋งŒ์˜ ๊ณ ์œ ํ•˜๊ณ  ์ „๋ฌธํ™”๋œ ์˜์—ญ์— ์ง‘์ค‘ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ํ•˜์ง€๋งŒ ๊ฐ ์ „๋ฌธ๊ฐ€๊ฐ€ โ€˜๊ณ ์œ ํ•œ ์ž์‹ ๋งŒ์˜ ์˜์—ญโ€™์— ํšจ๊ณผ์ ์œผ๋กœ ์ง‘์ค‘ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋Š”๋ฐ๋Š” ๋‚œ์ ์ด ์žˆ๋‹ค๋Š” ๋ฌธ์ œ ์—ญ์‹œ ์žˆ์Šต๋‹ˆ๋‹ค. ์„ ์กฐํ•ฉํ•ด์„œ ๊ฐœ์„ ํ•จ์œผ๋กœ์จ ์ˆ˜ํ•™ ๊ด€๋ จ ๋ฒค์น˜๋งˆํฌ์—์„œ์˜ ์„ฑ๋Šฅ์„ ์ƒ๋‹นํžˆ ๊ฐœ์„ ํ–ˆ์Šต๋‹ˆ๋‹ค - ๊ณ ๋“ฑํ•™๊ต ์ˆ˜์ค€์˜ miniF2F ํ…Œ์ŠคํŠธ์—์„œ 63.5%, ํ•™๋ถ€ ์ˆ˜์ค€์˜ ProofNet ํ…Œ์ŠคํŠธ์—์„œ 25.3%์˜ ํ•ฉ๊ฒฉ๋ฅ ์„ ๋‚˜ํƒ€๋‚ด๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๋ฌผ๋ก  ํ—ˆ๊น…ํŽ˜์ด์Šค์— ์˜ฌ๋ผ์™€ ์žˆ๋Š” ๋ชจ๋ธ์˜ ์ˆ˜๊ฐ€ ์ „์ฒด์ ์ธ ํšŒ์‚ฌ์˜ ์—ญ๋Ÿ‰์ด๋‚˜ ๋ชจ๋ธ์˜ ์ˆ˜์ค€์— ๋Œ€ํ•œ ์ง์ ‘์ ์ธ ์ง€ํ‘œ๊ฐ€ ๋  ์ˆ˜๋Š” ์—†๊ฒ ์ง€๋งŒ, DeepSeek์ด๋ผ๋Š” ํšŒ์‚ฌ๊ฐ€ โ€˜๋ฌด์—‡์„ ํ•ด์•ผ ํ•˜๋Š”๊ฐ€์— ๋Œ€ํ•œ ์–ด๋Š ์ •๋„ ๋ช…ํ™•ํ•œ ๊ทธ๋ฆผ์„ ๊ฐ€์ง€๊ณ  ๋น ๋ฅด๊ฒŒ ์‹คํ—˜์„ ๋ฐ˜๋ณตํ•ด ๊ฐ€๋ฉด์„œ ๋ชจ๋ธ์„ ์ถœ์‹œโ€™ํ•˜๋Š”๊ตฌ๋‚˜ ์ง์ž‘ํ•  ์ˆ˜๋Š” ์žˆ์Šต๋‹ˆ๋‹ค. ๋ฐ”๋กœ ์งํ›„์ธ 2023๋…„ 11์›” 29์ผ, DeepSeek LLM ๋ชจ๋ธ์„ ๋ฐœํ‘œํ–ˆ๋Š”๋ฐ, ์ด ๋ชจ๋ธ์„ โ€˜์ฐจ์„ธ๋Œ€์˜ ์˜คํ”ˆ์†Œ์Šค LLMโ€™์ด๋ผ๊ณ  ๋ถˆ๋ €์Šต๋‹ˆ๋‹ค. DeepSeek-Coder-V2 ๋ชจ๋ธ์€ ์ˆ˜ํ•™๊ณผ ์ฝ”๋”ฉ ์ž‘์—…์—์„œ ๋Œ€๋ถ€๋ถ„์˜ ๋ชจ๋ธ์„ ๋Šฅ๊ฐ€ํ•˜๋Š” ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ฃผ๋Š”๋ฐ, Qwen์ด๋‚˜ Moonshot ๊ฐ™์€ ์ค‘๊ตญ๊ณ„ ๋ชจ๋ธ๋“ค๋„ ํฌ๊ฒŒ ์•ž์„ญ๋‹ˆ๋‹ค.

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