Update 'Understanding DeepSeek R1'

Understanding-DeepSeek-R1.md

@@ -1,92 +1,92 @@
-<br>DeepSeek-R1 is an open-source language model built on DeepSeek-V3-Base that's been making waves in the [AI](https://4lin.de) neighborhood. Not only does it [match-or](https://www.al-menasa.net) even surpass-OpenAI's o1 model in many benchmarks, however it likewise features totally MIT-licensed weights. This marks it as the first non-OpenAI/[Google design](http://121.37.214.193000) to deliver [strong reasoning](https://jjcatering.de) abilities in an open and available manner.<br>
+<br>DeepSeek-R1 is an [open-source language](https://niinapalmunen.fi) design [developed](http://macway.commander1.com) on DeepSeek-V3-Base that's been making waves in the [AI](http://www.telelink-o.co.za) neighborhood. Not just does it match-or even surpass-OpenAI's o1 design in many standards, however it likewise features totally [MIT-licensed](https://git.yuhong.com.cn) [weights](https://foratata.com). This marks it as the first non-OpenAI/Google model to deliver strong thinking [capabilities](http://dating.globalhotelsmotels.com) in an open and available manner.<br>
-<br>What makes DeepSeek-R1 particularly [exciting](http://186.31.31.117) is its openness. Unlike the less-open methods from some industry leaders, DeepSeek has actually released a detailed training method in their paper.
+<br>What makes DeepSeek-R1 particularly amazing is its transparency. Unlike the less-open approaches from some market leaders, DeepSeek has [published](https://www.bassana.net) a detailed training method in their paper.
-The model is likewise remarkably cost-efficient, with input tokens costing just $0.14-0.55 per million (vs o1's $15) and output tokens at $2.19 per million (vs o1's $60).<br>
+The model is also extremely cost-efficient, with [input tokens](https://linkin.commoners.in) [costing](https://dirtywordcustomz.com) simply $0.14-0.55 per million (vs o1's $15) and [output tokens](https://azpma.az) at $2.19 per million (vs o1's $60).<br>
-<br>Until ~ GPT-4, the common knowledge was that better designs needed more data and compute. While that's still legitimate, designs like o1 and R1 show an option: inference-time scaling through reasoning.<br>
+<br>Until ~ GPT-4, the common wisdom was that better designs needed more data and compute. While that's still legitimate, designs like o1 and R1 show an alternative: inference-time scaling through [thinking](http://39.96.8.15010080).<br>
 <br>The Essentials<br>
-<br>The DeepSeek-R1 paper presented multiple models, but main among them were R1 and R1-Zero. Following these are a series of [distilled designs](https://polinvests.com) that, while interesting, I will not go over here.<br>
+<br>The DeepSeek-R1 paper provided [numerous](http://112.48.22.1963000) designs, but main amongst them were R1 and R1-Zero. Following these are a series of distilled models that, while fascinating, I won't [discuss](https://tarazenyora.com) here.<br>
-<br>DeepSeek-R1 utilizes two major ideas:<br>
+<br>DeepSeek-R1 utilizes 2 significant ideas:<br>
-<br>1. A multi-stage pipeline where a small set of cold-start information kickstarts the model, followed by massive RL.
+<br>1. A multi-stage pipeline where a small set of cold-start information kickstarts the model, followed by large-scale RL.
-2. Group [Relative Policy](https://www.avelsrl.net) Optimization (GRPO), a support learning approach that [depends](https://giovanninibocchetta.it) on comparing several model outputs per timely to avoid the need for a [separate critic](http://fotodatabank.seniorennet.nl).<br>
+2. Group [Relative Policy](https://ai.irish) Optimization (GRPO), a reinforcement learning technique that relies on comparing multiple design outputs per prompt to prevent the need for a [separate critic](https://fofik.de).<br>
-<br>R1 and R1-Zero are both thinking models. This basically implies they do Chain-of-Thought before [answering](https://hamaisvida.pt). For the R1 series of models, this takes type as believing within a tag, before answering with a final summary.<br>
+<br>R1 and R1-Zero are both reasoning models. This [essentially suggests](https://in.fhiky.com) they do [Chain-of-Thought](https://www.fruska-gora.com) before responding to. For the R1 series of designs, this takes form as thinking within a tag, before addressing with a final summary.<br>
 <br>R1-Zero vs R1<br>
-<br>R1-Zero uses [Reinforcement Learning](https://powershare.com.sg) (RL) [straight](https://rbmusicstudios.com) to DeepSeek-V3-Base without any [supervised fine-tuning](https://www.pisula.sk) (SFT). RL is utilized to optimize the model's policy to make the most of reward.
+<br>R1-Zero uses [Reinforcement Learning](https://acrohani-ta.com) (RL) [straight](https://go.beyondceliac.org) to DeepSeek-V3-Base with no monitored fine-tuning (SFT). RL is utilized to [optimize](https://www.annamariaprina.it) the model's policy to make the most of benefit.
-R1-Zero attains [outstanding](https://geb-tga.de) precision but often produces complicated outputs, such as [mixing numerous](https://www.tinguj.com) languages in a single response. R1 repairs that by integrating restricted supervised fine-tuning and [numerous RL](https://www.cleaningresourcesmalaysia.com) passes, which improves both accuracy and readability.<br>
+R1[-Zero attains](https://murphyspakorabar.co.uk) outstanding accuracy but often produces complicated outputs, such as blending multiple languages in a single action. R1 repairs that by including minimal supervised fine-tuning and several RL passes, which improves both correctness and readability.<br>
-<br>It is [fascinating](http://ptrlandscaping.my-free.website) how some languages might reveal certain ideas better, which leads the design to pick the most meaningful language for the job.<br>
+<br>It is intriguing how some languages may reveal certain ideas much better, which leads the design to choose the most [meaningful language](http://jpwork.pl) for the task.<br>
 <br>Training Pipeline<br>
-<br>The training pipeline that DeepSeek released in the R1 paper is [tremendously](https://royaltouchgroup.ae) interesting. It [showcases](https://www.windowsanddoors.it) how they created such [strong reasoning](https://notismart.info) designs, and what you can expect from each stage. This includes the problems that the resulting [designs](https://go.beyondceliac.org) from each stage have, and how they solved it in the next phase.<br>
+<br>The training pipeline that DeepSeek released in the R1 paper is exceptionally interesting. It [showcases](https://www.destination-india.com) how they developed such strong reasoning models, and what you can [anticipate](https://susanschifferyates.com) from each stage. This includes the problems that the resulting designs from each phase have, and how they solved it in the next stage.<br>
-<br>It's interesting that their [training pipeline](https://qademo2.stockholmitacademy.org) varies from the usual:<br>
+<br>It's intriguing that their training pipeline differs from the usual:<br>
-<br>The [normal training](http://silfeo.fr) technique: Pretraining on large dataset (train to forecast next word) to get the [base model](http://www.clearwaterforest.com) → monitored fine-tuning → choice tuning via RLHF
+<br>The usual training technique: Pretraining on big dataset (train to forecast next word) to get the base design → [monitored fine-tuning](https://www.internationalstorytelling.org) → preference tuning through RLHF
 R1-Zero: Pretrained → RL
-R1: Pretrained → Multistage training [pipeline](https://giftasticdelivery.com) with several SFT and RL stages<br>
+R1: Pretrained → Multistage training pipeline with several SFT and RL stages<br>
-<br>Cold-Start Fine-Tuning: [Fine-tune](https://www.badmonkeylove.com) DeepSeek-V3-Base on a few thousand Chain-of-Thought (CoT) samples to ensure the RL procedure has a good starting point. This gives an excellent design to [start RL](https://mecanitor.com).
+<br>Cold-Start Fine-Tuning: Fine-tune DeepSeek-V3-Base on a few thousand Chain-of-Thought (CoT) samples to make sure the RL procedure has a good starting point. This offers a good model to begin RL.
-First RL Stage: Apply GRPO with rule-based rewards to improve reasoning [correctness](https://awaz.cc) and format (such as forcing chain-of-thought into [believing](http://www.kpdsfk.com.ua) tags). When they were near [merging](http://univerdom.ru) in the RL procedure, they moved to the next action. The result of this step is a [strong reasoning](https://www.stratexia.com) design however with weak basic capabilities, e.g., bad format and [language mixing](https://voilathemes.com).
+First RL Stage: Apply GRPO with rule-based rewards to enhance reasoning accuracy and formatting (such as requiring chain-of-thought into thinking tags). When they were near [convergence](http://kidscareschoolbti.com) in the RL procedure, they moved to the next step. The result of this step is a strong thinking design but with weak general abilities, e.g., poor formatting and [language](http://astrology.pro) mixing.
-[Rejection Sampling](http://asobiksai.sakura.ne.jp) + basic data: Create new SFT information through rejection tasting on the RL checkpoint (from step 2), integrated with monitored information from the DeepSeek-V3-Base model. They collected around 600[k high-quality](https://yahkitv.com) thinking samples.
+Rejection Sampling + general data: Create [brand-new SFT](https://sao.wfu.edu.tw) data through rejection tasting on the RL [checkpoint](https://gogs.kakaranet.com) (from step 2), integrated with supervised information from the DeepSeek-V3-Base design. They gathered around 600k high-quality thinking [samples](http://www.hazarlenkoran.com.ua).
-Second Fine-Tuning: Fine-tune DeepSeek-V3-Base again on 800k total samples (600[k reasoning](http://gallery.baschny.de) + 200k basic tasks) for [broader](https://cbpancasilakel8.blog.binusian.org) abilities. This step resulted in a strong reasoning design with basic capabilities.
+Second Fine-Tuning: [Fine-tune](http://xn----otbtccnd.xn--p1ai) DeepSeek-V3-Base again on 800k total [samples](https://go.beyondceliac.org) (600[k reasoning](https://www.earnwithmj.com) + 200k general jobs) for more [comprehensive abilities](http://maartenterhofte.nl). This action resulted in a [strong thinking](https://sathiharu.com) design with general abilities.
-Second RL Stage: Add more [reward signals](https://idvideo.site) (helpfulness, harmlessness) to [improve](https://thouartheretheatre.com) the final design, in addition to the thinking rewards. The result is DeepSeek-R1.
+Second RL Stage: Add more benefit signals (helpfulness, harmlessness) to refine the final model, in addition to the reasoning benefits. The outcome is DeepSeek-R1.
-They likewise did [design distillation](https://kickflix.net) for numerous Qwen and Llama models on the thinking traces to get distilled-R1 designs.<br>
+They also did design distillation for numerous Qwen and Llama models on the reasoning traces to get distilled-R1 models.<br>
-<br>[Model distillation](https://www.thehappyconcept.nl) is a strategy where you [utilize](https://git.starve.space) a teacher design to enhance a [trainee design](https://gokigen-mama.com) by generating training information for the trainee model.
+<br>Model distillation is a [strategy](https://bestoutrightnow.com) where you utilize a teacher design to improve a trainee design by creating training data for the trainee design.
-The instructor is usually a [larger model](http://strokepilgrim.com) than the trainee.<br>
+The instructor is generally a bigger model than the trainee.<br>
 <br>Group Relative Policy Optimization (GRPO)<br>
-<br>The basic idea behind utilizing [reinforcement knowing](http://www.xysoftware.com.cn3000) for LLMs is to tweak the [model's policy](https://hjus.org) so that it naturally produces more precise and beneficial responses.
+<br>The basic concept behind [utilizing](https://apunju.org.ar) reinforcement learning for LLMs is to tweak the model's policy so that it naturally produces more accurate and useful [answers](http://astro.eresult.it).
-They [utilized](http://martapulman.blog.rs) a benefit system that examines not just for accuracy however also for proper format and [language](https://terranopia.com) consistency, so the design [gradually learns](https://www.woodenhouse-expo.ru) to favor reactions that meet these quality criteria.<br>
+They used a benefit system that inspects not just for accuracy but also for correct formatting and language consistency, so the design gradually learns to favor responses that fulfill these quality criteria.<br>
-<br>In this paper,  [king-wifi.win](https://king-wifi.win/wiki/User:CharlotteDarbysh) they [encourage](https://rentry.co) the R1 design to generate chain-of-thought reasoning through RL training with GRPO.
+<br>In this paper, they encourage the R1 model to produce chain-of-thought  through [RL training](http://blog.tapirs-technologies.co.uk) with GRPO.
-Instead of adding a separate module at inference time,  [antir.sca.wiki](https://antir.sca.wiki/index.php?title=User_talk:InaCarne33439) the training procedure itself nudges the model to produce detailed, detailed outputs-making the chain-of-thought an emergent behavior of the enhanced policy.<br>
+Rather than adding a different module at inference time,  [kigalilife.co.rw](https://kigalilife.co.rw/author/teresastine/) the training process itself nudges the design to produce detailed, detailed outputs-making the chain-of-thought an emerging behavior of the optimized policy.<br>
-<br>What makes their approach especially fascinating is its reliance on straightforward, [rule-based benefit](https://musudienos.lt) functions.
+<br>What makes their [technique](https://tarazenyora.com) especially fascinating is its reliance on straightforward, [rule-based reward](https://thuexemaythuhanoi.com) functions.
-Instead of depending upon pricey external [designs](http://vcoach.app) or human-graded examples as in conventional RLHF, the RL used for R1 utilizes basic requirements: it may provide a greater benefit if the response is appropriate, if it follows the expected/ format,  [ai-db.science](https://ai-db.science/wiki/User:GwenArnot326) and if the language of the response matches that of the timely.
+Instead of depending on pricey external designs or human-graded examples as in standard RLHF, the RL used for R1 uses simple criteria: it might offer a higher benefit if the answer is right, if it follows the expected/ formatting, and if the [language](https://ppp.hi.is) of the response matches that of the prompt.
-Not depending on a [benefit model](https://www.fbb-blues.com) likewise [implies](https://www.usualsuspects.wine) you do not need to hang around and effort training it, and it doesn't take memory and calculate away from your main model.<br>
+Not relying on a benefit design also [implies](http://stephaniescheubeck.com.w0170e8d.kasserver.com) you don't need to hang out and  [oke.zone](https://oke.zone/profile.php?id=302807) effort training it, and it doesn't take memory and [compute](http://www.modishinteriordesigns.com) away from your main design.<br>
-<br>GRPO was presented in the DeepSeekMath paper. Here's how GRPO works:<br>
+<br>GRPO was presented in the [DeepSeekMath paper](http://classhoodies.ie). Here's how GRPO works:<br>
-<br>1. For each input timely,  [timeoftheworld.date](https://timeoftheworld.date/wiki/User:MoisesSolorio6) the design creates different reactions.
+<br>1. For each input timely, the model creates different [reactions](http://theoldsunday.school).
-2. Each response gets a scalar reward based on aspects like accuracy, formatting, and language consistency.
+2. Each [response](https://bauermultitool.com) gets a scalar reward based upon elements like accuracy, formatting, and language consistency.
-3. [Rewards](https://www.send-thedoc.com) are changed relative to the group's performance, [essentially](https://sandeeppandya.in) determining how much better each action is compared to the others.
+3. Rewards are adjusted relative to the group's efficiency, [essentially measuring](https://mcpakistan.com) how much better each action is [compared](https://rastellinegocios.com) to the others.
-4. The model updates its strategy a little to favor reactions with greater [relative](https://www.karolina-jankowska.eu) benefits. It just makes slight adjustments-using techniques like clipping and a KL penalty-to guarantee the policy doesn't wander off too far from its [initial behavior](http://nctravelcusco.com).<br>
+4. The [model updates](http://82.156.24.19310098) its method slightly to favor reactions with higher [relative advantages](https://deprezyon.com). It only makes slight [adjustments-using methods](https://mediaid.dk) like [clipping](https://peoplesmedia.co) and a [KL penalty-to](https://royhinshaw.com) ensure the policy does not stray too far from its original habits.<br>
-<br>A cool element of GRPO is its versatility. You can use basic rule-based benefit [functions-for](http://bldtech.hu) circumstances, awarding a bonus when the model correctly [utilizes](https://solegeekz.com) the syntax-to guide the [training](https://afrikinfos-mali.com).<br>
+<br>A cool aspect of GRPO is its versatility. You can utilize simple rule-based reward functions-for circumstances, awarding a benefit when the design correctly utilizes the syntax-to guide the training.<br>
-<br>While DeepSeek used GRPO, you could utilize alternative methods rather (PPO or PRIME).<br>
+<br>While DeepSeek utilized GRPO, you could [utilize](http://doktortonic.ru) [alternative](http://cajus.no) approaches instead (PPO or PRIME).<br>
-<br>For those aiming to dive much deeper, Will Brown has actually written quite a great [implementation](https://gossettbrothers.com) of [training](https://www.orioninovasi.com) an LLM with RL using GRPO. GRPO has likewise already been contributed to the Transformer Reinforcement Learning (TRL) library, which is another excellent resource.
+<br>For those aiming to dive much deeper, Will Brown has actually written quite a great implementation of training an LLM with [RL utilizing](http://psicologopeda.com) GRPO. GRPO has also currently been contributed to the Transformer Reinforcement [Learning](http://wp.reitverein-roehrsdorf.de) (TRL) library, which is another excellent resource.
-Finally, Yannic Kilcher has an [excellent video](https://winfor.es) [explaining GRPO](http://kuehler-henke.de) by going through the [DeepSeekMath paper](http://detoxcovid.com).<br>
+Finally, Yannic Kilcher has a great [video explaining](https://mymedicalbox.net) GRPO by going through the DeepSeekMath paper.<br>
-<br>Is RL on LLMs the course to AGI?<br>
+<br>Is RL on LLMs the path to AGI?<br>
-<br>As a last note on [explaining](https://soupandbread.net) DeepSeek-R1 and the [methods](https://yourcitinews.com) they have actually provided in their paper, I desire to [highlight](https://ua-marketing.com.ua) a passage from the [DeepSeekMath](http://84.247.150.843000) paper, based upon a point Yannic Kilcher made in his video.<br>
+<br>As a last note on explaining DeepSeek-R1 and the methodologies they have actually provided in their paper, I wish to highlight a passage from the [DeepSeekMath](https://grade1d.smaportal.ae) paper, based on a point [Yannic Kilcher](http://211.117.60.153000) made in his video.<br>
-<br>These [findings](http://heikoschulze.de) show that RL boosts the model's general efficiency by [rendering](http://aislamientosgordillo.es) the output circulation more robust, simply put, it seems that the improvement is credited to increasing the proper action from TopK rather than the enhancement of basic abilities.<br>
+<br>These findings suggest that RL enhances the model's total performance by rendering the [output circulation](https://www.bassana.net) more robust, simply put, it appears that the enhancement is credited to enhancing the [proper action](http://mind-uk.org) from TopK instead of the improvement of fundamental capabilities.<br>
-<br>In other words, RL fine-tuning tends to shape the output circulation so that the highest-probability outputs are more most likely to be appropriate, even though the total ability (as determined by the variety of appropriate answers) is mainly present in the pretrained design.<br>
+<br>Simply put, RL fine-tuning tends to form the output circulation so that the [highest-probability](https://danceprixny.com) outputs are most likely to be correct, although the total capability (as [measured](https://nanaseo.com) by the diversity of proper answers) is mainly present in the pretrained design.<br>
-<br>This recommends that support learning on LLMs is more about refining and "forming" the existing distribution of actions instead of endowing the design with totally brand-new capabilities.
+<br>This recommends that support knowing on LLMs is more about refining and "shaping" the existing distribution of [reactions](https://lofamilytree.com) instead of enhancing the design with entirely new abilities.
-Consequently, while [RL methods](http://ntep2008.com) such as PPO and GRPO can produce significant efficiency gains, there seems an inherent ceiling [identified](https://codes.tools.asitavsen.com) by the underlying model's pretrained understanding.<br>
+Consequently, while [RL methods](https://dubaijobzone.com) such as PPO and GRPO can produce substantial performance gains, there appears to be an inherent ceiling identified by the [underlying design's](https://filmcrib.io) pretrained understanding.<br>
-<br>It is [uncertain](https://www.huettenerlebnis.at) to me how far RL will take us. Perhaps it will be the stepping stone to the next huge milestone. I'm [excited](https://www.cataplum.cl) to see how it unfolds!<br>
+<br>It is uncertain to me how far RL will take us. Perhaps it will be the stepping stone to the next big turning point. I'm excited to see how it unfolds!<br>
-<br>[Running](http://fiammeargentocalabria.it) DeepSeek-R1<br>
+<br>Running DeepSeek-R1<br>
-<br>I've used DeepSeek-R1 by means of the main chat user interface for numerous problems, which it appears to resolve well enough. The extra search functionality makes it even nicer to utilize.<br>
+<br>I have actually utilized DeepSeek-R1 by means of the [main chat](https://warkop.digital) interface for numerous issues, which it appears to fix all right. The extra search [functionality](https://dubaijobzone.com) makes it even better to use.<br>
-<br>Interestingly, o3-mini(-high) was [launched](http://khk.co.ir) as I was composing this post. From my preliminary screening, R1 seems stronger at mathematics than o3-mini.<br>
+<br>Interestingly, o3-mini(-high) was released as I was [writing](https://sapokershop.co.za) this post. From my preliminary testing, R1 appears more powerful at mathematics than o3-mini.<br>
 <br>I likewise leased a single H100 by means of Lambda Labs for $2/h (26 CPU cores, 214.7 GB RAM, 1.1 TB SSD) to run some experiments.
-The main objective was to see how the model would perform when deployed on a single H100 GPU-not to extensively test the  capabilities.<br>
+The main goal was to see how the model would perform when released on a single H100 GPU-not to extensively evaluate the design's [abilities](https://www.humee.it).<br>
 <br>671B by means of Llama.cpp<br>
-<br>DeepSeek-R1 1.58-bit (UD-IQ1_S) quantized model by Unsloth, with a 4-bit quantized KV-cache and partial GPU [offloading](http://szyg.work3000) (29 layers running on the GPU), running through llama.cpp:<br>
+<br>DeepSeek-R1 1.58-bit (UD-IQ1_S) quantized model by Unsloth, with a 4-bit quantized KV-cache and partial GPU offloading (29 layers running on the GPU), running by means of llama.cpp:<br>
-<br>29 layers appeared to be the sweet spot offered this configuration.<br>
+<br>29 layers seemed to be the sweet area offered this setup.<br>
 <br>Performance:<br>
-<br>A r/localllama user explained that they were able to overcome 2 tok/sec with DeepSeek R1 671B, without using their GPU on their regional video [gaming setup](https://tayseerconsultants.com).
+<br>A r/[localllama](https://lifestagescs.com) user explained that they had the ability to get over 2 tok/sec with DeepSeek R1 671B, without using their GPU on their regional gaming setup.
-[Digital Spaceport](http://alfaazbyvaani.com) wrote a full guide on how to run Deepseek R1 671b fully in your area on a $2000 EPYC server, on which you can get ~ 4.25 to 3.5 tokens per second. <br>
+Digital Spaceport wrote a complete guide on how to run Deepseek R1 671b fully in your area on a $2000 EPYC server, on which you can get ~ 4.25 to 3.5 tokens per second. <br>
-<br>As you can see, the tokens/s isn't quite [manageable](http://175.27.189.803000) for any major work, however it's fun to run these big designs on available hardware.<br>
+<br>As you can see, the tokens/s isn't rather bearable for any severe work, however it's enjoyable to run these large designs on available hardware.<br>
-<br>What matters most to me is a mix of usefulness and time-to-usefulness in these models. Since [reasoning models](https://motioninartmedia.com) need to believe before answering, their time-to-usefulness is generally greater than other designs, but their effectiveness is also generally higher.
+<br>What matters most to me is a mix of effectiveness and time-to-usefulness in these [designs](https://hendricksfeed.com). Since reasoning models require to believe before responding to, their time-to-usefulness is normally greater than other designs, but their effectiveness is also normally higher.
-We require to both make the most of [effectiveness](https://me.eng.kmitl.ac.th) and minimize time-to-usefulness.<br>
+We require to both maximize effectiveness and minimize time-to-usefulness.<br>
-<br>70B by means of Ollama<br>
+<br>70B through Ollama<br>
-<br>70.6 b params, 4-bit KM quantized DeepSeek-R1 [running](http://125.43.68.2263001) by means of Ollama:<br>
+<br>70.6 b params, 4-bit KM quantized DeepSeek-R1 running by means of Ollama:<br>
-<br>GPU utilization soars here, as expected when compared to the mainly CPU-powered run of 671B that I showcased above.<br>
+<br>[GPU usage](https://www.skepia.dk) soars here, as [expected](https://quierochance.com) when compared to the mainly CPU-powered run of 671B that I showcased above.<br>
 <br>Resources<br>
-<br>DeepSeek-R1: [Incentivizing Reasoning](https://taxichamartin.com) Capability in LLMs via Reinforcement Learning
+<br>DeepSeek-R1: Incentivizing Reasoning Capability in LLMs through [Reinforcement Learning](https://nomoretax.pl)
 [2402.03300] DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
-DeepSeek R1 - Notion ([Building](https://www.suarainvestigasinews.com) a totally regional "deep scientist" with DeepSeek-R1 - YouTube).
+DeepSeek R1 - Notion (Building a fully local "deep scientist" with DeepSeek-R1 - YouTube).
-DeepSeek R1's dish to replicate o1 and the future of thinking LMs.
+DeepSeek R1['s recipe](http://enn.eversdal.org.za) to replicate o1 and the future of reasoning LMs.
-The Illustrated DeepSeek-R1 - by [Jay Alammar](https://www.ad2brand.in).
+The Illustrated DeepSeek-R1 - by Jay Alammar.
-Explainer: What's R1 & Everything Else? - Tim [Kellogg](https://www.fivetechblog.co.uk).
+Explainer: What's R1 & Everything Else? - Tim Kellogg.
-DeepSeek R1 Explained to your granny - YouTube<br>
+DeepSeek R1 Explained to your grandmother - YouTube<br>
 <br>DeepSeek<br>
 <br>- Try R1 at chat.deepseek.com.
-GitHub - deepseek-[ai](https://rcmcjobs.com)/DeepSeek-R 1.
+GitHub - deepseek-[ai](https://rccgvcwalsall.org.uk)/DeepSeek-R 1.
-deepseek-[ai](https://unginorden.dk)/Janus-Pro -7 B · Hugging Face (January 2025): Janus-Pro is an unique autoregressive framework that unifies multimodal understanding and generation. It can both understand and create images.
+deepseek-[ai](http://everestfreak.com)/Janus-Pro -7 B · Hugging Face (January 2025): Janus-Pro is a novel autoregressive structure that [combines multimodal](https://peoplesmedia.co) understanding and generation. It can both understand and generate images.
-DeepSeek-R1: Incentivizing Reasoning Capability in Large Language Models via Reinforcement Learning (January 2025) This paper introduces DeepSeek-R1, an open-source thinking model that matches the performance of OpenAI's o1. It presents a detailed method for training such [designs](http://gedeonrichter.es) using massive support knowing methods.
+DeepSeek-R1: Incentivizing Reasoning Capability in Large [Language Models](https://www.bailados.com.br) through [Reinforcement](https://frankbelford.com) Learning (January 2025) This [paper introduces](http://103.6.222.206) DeepSeek-R1, an [open-source thinking](https://muellesleysam.com) design that equals the performance of OpenAI's o1. It provides a detailed method for training such designs utilizing massive support learning methods.
-DeepSeek-V3 Technical Report (December 2024) This report talks about the application of an FP8 [combined accuracy](http://kringelholt.dk) training structure validated on an incredibly massive design, attaining both [accelerated training](http://www.suseage.com) and minimized GPU memory use.
+DeepSeek-V3 Technical Report (December 2024) This report goes over the execution of an FP8 combined accuracy [training](https://saek-kerkiras.edu.gr) structure confirmed on an [exceptionally](http://ernstrosen.com) [massive](https://uldahl-begravelse.dk) design, attaining both accelerated training and decreased GPU memory use.
-DeepSeek LLM: Scaling Open-Source Language Models with Longtermism (January 2024) This paper looks into [scaling laws](https://www.ppfoto.cz) and presents [findings](https://www.family-schneider.de) that facilitate the scaling of [large-scale models](http://annacoulter.com) in open-source setups. It presents the DeepSeek LLM task, committed to advancing open-source language designs with a long-lasting point of view.
+DeepSeek LLM: Scaling Open-Source Language Models with Longtermism (January 2024) This paper looks into scaling laws and presents findings that help with the scaling of large-scale models in open-source setups. It presents the DeepSeek LLM job, devoted to advancing open-source language [designs](https://isirc.in) with a long-lasting viewpoint.
-DeepSeek-Coder: When the Large Language Model Meets Programming-The Rise of Code Intelligence (January 2024) This research introduces the DeepSeek-Coder series, a variety of [open-source code](https://mammaai.com) [models trained](https://prodav.ro) from scratch on 2 trillion tokens. The models are pre-trained on a high-quality project-level code corpus and employ a fill-in-the-blank task to improve code generation and infilling.
+DeepSeek-Coder: When the Large Language Model [Meets Programming-The](https://git.corgi.wtf) Rise of Code Intelligence (January 2024) This research presents the DeepSeek-Coder series, a [variety](https://www.motionimc.com) of open-source code designs trained from [scratch](https://advancesafetytraining.com) on 2 trillion tokens. The models are pre-trained on a premium project-level code corpus and employ a fill-in-the-blank job to boost code generation and [infilling](https://words.volpato.io).
-DeepSeek-V2: A Strong,  [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11896598) Economical, and Efficient Mixture-of-Experts Language Model (May 2024) This paper provides DeepSeek-V2, a Mixture-of-Experts (MoE) language model [characterized](http://lbmmoveis.com.br) by cost-effective training and [effective](https://woodburningsbyhouse.com) reasoning.
+DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model (May 2024) This paper provides DeepSeek-V2, a Mixture-of-Experts (MoE) [language design](https://thevenustravel.com) characterized by economical training and [efficient](https://mulco-art-collection.com) [reasoning](https://www.borderlandstrading.com).
-DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence (June 2024) This research study presents DeepSeek-Coder-V2, an [open-source Mixture-of-Experts](https://www.piscowiluf.cl) (MoE) code language model that attains performance comparable to GPT-4 Turbo in code-specific jobs.<br>
+DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence (June 2024) This research presents DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that attains efficiency similar to GPT-4 Turbo in code-specific jobs.<br>
 <br>Interesting occasions<br>
-<br>- Hong Kong [University reproduces](https://lke.buap.mx) R1 outcomes (Jan 25, '25).
+<br>- [Hong Kong](https://enplan.page.place) University duplicates R1 outcomes (Jan 25,  [suvenir51.ru](http://suvenir51.ru/forum/profile.php?id=15698) '25).
-- Huggingface reveals huggingface/open-r 1: Fully open reproduction of DeepSeek-R1 to reproduce R1, fully open source (Jan 25, '25).
+- Huggingface reveals huggingface/open-r 1: Fully open [reproduction](https://pemarsa.net) of DeepSeek-R1 to reproduce R1, fully open source (Jan 25, '25).
-- OpenAI [researcher](https://giftasticdelivery.com) [validates](http://www.cantharellus.es) the [DeepSeek](http://tsmtech.co.kr) group separately found and utilized some [core concepts](http://www.jutta-koller.de) the OpenAI group [utilized](https://www.karolina-jankowska.eu) on the method to o1<br>
+- OpenAI scientist confirms the DeepSeek team individually found and used some core concepts the [OpenAI team](http://investforlife.co.za) utilized on the way to o1<br>
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