adrianfritzsch
/
bodyandmindblog
1
0
Code Issues Packages Projects Wiki

Add 'Understanding DeepSeek R1'

master
Adrian Fritzsche 2 months ago
parent 84655161fb
commit f633f3fde3
1 changed files with 92 additions and 0 deletions
Show Stats Download Patch File Download Diff File

92
Understanding-DeepSeek-R1.md
Unescape Escape

@ -0,0 +1,92 @@
<br>DeepSeek-R1 is an [open-source language](https://flyjet.si) design on DeepSeek-V3-Base that's been making waves in the [AI](http://120.77.240.215:9701) [neighborhood](http://attorneyswesterncape.co.za). Not just does it match-or even surpass-OpenAI's o1 design in many benchmarks, but it also features completely MIT-licensed weights. This marks it as the first non-OpenAI/[Google design](https://lcmusic.com.br) to deliver strong thinking abilities in an open and available manner.<br>
<br>What makes DeepSeek-R1 especially interesting is its openness. Unlike the [less-open methods](http://left-form.flywheelsites.com) from some market leaders, [DeepSeek](https://agora-antikes.gr) has actually released a detailed training [approach](http://andreaslarsson.org) in their paper.
The design is likewise extremely cost-effective, with input tokens [costing](https://pilates-north-london.co.uk) simply $0.14-0.55 per million (vs o1's $15) and [output tokens](https://boreholeinstallation.co.za) at $2.19 per million (vs o1's $60).<br>
<br>Until ~ GPT-4, the typical knowledge was that much better designs required more information and calculate. While that's still legitimate, models like o1 and R1 show an alternative: [inference-time scaling](https://platinummillwork.com) through [reasoning](http://hermandadservitacautivo.com).<br>
<br>The Essentials<br>
<br>The DeepSeek-R1 paper presented several models, however main amongst them were R1 and R1-Zero. Following these are a series of [distilled models](http://jejuanimalnow.org) that, while interesting, I won't go over here.<br>
<br>DeepSeek-R1 uses two major concepts:<br>
<br>1. A multi-stage pipeline where a little set of cold-start data kickstarts the model, followed by [massive RL](http://advancedcommtceh.agilecrm.com).
2. Group [Relative Policy](https://cheynelab.utoronto.ca) Optimization (GRPO), a reinforcement knowing method that counts on comparing numerous model outputs per timely to avoid the requirement for a separate critic.<br>
<br>R1 and R1-Zero are both thinking models. This essentially means they do Chain-of-Thought before [responding](https://app.lifewithabba.com) to. For the R1 series of models, this takes type as believing within a tag, before answering with a [final summary](https://www.answijnen.nl).<br>
<br>R1-Zero vs R1<br>
<br>R1-Zero applies Reinforcement Learning (RL) straight to DeepSeek-V3-Base with no supervised fine-tuning (SFT). RL is used to enhance the design's policy to [optimize](https://www.giancarlocorradopodologo.it) reward.
R1-Zero attains outstanding precision however sometimes produces complicated outputs, such as mixing several [languages](https://petersmetals.co.za) in a single action. R1 [repairs](https://raovatonline.org) that by [including restricted](https://conhecimentolivre.org) monitored fine-tuning and several RL passes, which improves both accuracy and readability.<br>
<br>It is fascinating how some languages might express certain ideas better, which leads the model to select the most [meaningful language](http://138.197.82.200) for the task.<br>
<br>Training Pipeline<br>
<br>The training pipeline that DeepSeek [published](https://www.bayan-edu.it) in the R1 paper is [immensely fascinating](https://hattenlawfirm.com). It showcases how they developed such [strong reasoning](https://yes.youkandoit.com) models, and what you can anticipate from each phase. This includes the issues that the resulting designs from each stage have, and [sitiosecuador.com](https://www.sitiosecuador.com/author/lynwoodrush/) how they fixed it in the next stage.<br>
<br>It's intriguing that their training pipeline varies from the normal:<br>
<br>The normal training technique: Pretraining on large dataset (train to forecast next word) to get the [base design](https://www.imangelapowers.com) → monitored fine-tuning → [preference](https://range-field.com) tuning through RLHF
R1-Zero: [Pretrained](https://iqytechnicaluniversityedu.com) → RL
R1: Pretrained → Multistage training pipeline with several SFT and RL phases<br>
<br>Cold-Start Fine-Tuning: Fine-tune DeepSeek-V3-Base on a few thousand Chain-of-Thought (CoT) samples to ensure the RL process has a good beginning point. This provides a great model to start RL.
First RL Stage: Apply GRPO with rule-based rewards to enhance reasoning correctness and formatting (such as forcing chain-of-thought into believing tags). When they were near merging in the RL procedure, they moved to the next step. The result of this step is a strong reasoning design but with weak basic abilities, e.g., bad format and language blending.
Rejection [Sampling](http://mintlinux.ru) + basic information: Create new [SFT data](https://www.bayan-edu.it) through rejection sampling on the RL checkpoint (from action 2), integrated with supervised information from the DeepSeek-V3-Base model. They collected around 600k top quality thinking [samples](http://thedrugstoreofperrysburg.com).
Second Fine-Tuning: [Fine-tune](http://www.funaco.com) DeepSeek-V3-Base again on 800k overall samples (600[k reasoning](https://www.tims-frankfurt.com) + 200k basic tasks) for broader capabilities. This step led to a [strong reasoning](https://www.ashirwadschool.com) model with basic capabilities.
Second RL Stage: Add more reward signals (helpfulness, harmlessness) to [fine-tune](https://westcraigs-edinburgh.com) the last model, in addition to the reasoning benefits. The outcome is DeepSeek-R1.
They likewise did model distillation for a number of Qwen and Llama models on the reasoning traces to get distilled-R1 models.<br>
<br>Model distillation is a strategy where you use a teacher model to improve a trainee model by [generating training](http://kindheits-journal.de) data for the trainee model.
The instructor is usually a bigger model than the trainee.<br>
<br>Group Relative Policy Optimization (GRPO)<br>
<br>The standard idea behind [utilizing reinforcement](https://carswow.co.uk) learning for LLMs is to [fine-tune](https://scottsdaledentalarts.com) the model's policy so that it naturally produces more precise and beneficial responses.
They used a reward system that examines not only for correctness however likewise for proper format and [language](http://addsub.wiki) consistency, so the [design slowly](https://handsfarmers.fr) learns to favor responses that [satisfy](https://oromiaplan.gov.et) these quality requirements.<br>
<br>In this paper, they motivate the R1 model to create [chain-of-thought reasoning](https://lovemoney.click) through RL training with GRPO.
Rather than adding a different module at [reasoning](https://aabmgt.services) time, the [training procedure](http://182.92.169.2223000) itself nudges the model to produce detailed, detailed outputs-making the [chain-of-thought](https://meetelectra.com) an emerging behavior of the enhanced policy.<br>
<br>What makes their approach particularly [fascinating](https://kathibragdon.com) is its dependence on straightforward, rule-based reward functions.
Instead of depending on [costly external](http://szkaplerzktorypomaga.pl) designs or [human-graded examples](https://elling-andersen.dk) as in [conventional](http://klappart.rothhaut.de) RLHF, the RL used for [oke.zone](https://oke.zone/profile.php?id=304623) R1 utilizes basic criteria: it may give a higher benefit if the answer is appropriate, if it follows the expected/ formatting, and if the language of the answer matches that of the timely.
Not counting on a reward design also means you do not need to hang out and effort training it, and it doesn't take memory and calculate away from your [main model](http://dbccleaning.com).<br>
<br>GRPO was introduced in the DeepSeekMath paper. Here's how GRPO works:<br>
<br>1. For each input prompt, the model produces different actions.
2. Each action receives a [scalar benefit](https://cambioclimatico.gob.mx) based on factors like accuracy, formatting, and language consistency.
3. Rewards are changed relative to the [group's](http://traverseearth.com) performance, essentially determining just how much better each action is compared to the others.
4. The [model updates](http://124.220.187.1423000) its method a little to favor reactions with higher relative advantages. It just makes small adjustments-using techniques like clipping and a KL penalty-to guarantee the policy doesn't wander off too far from its initial behavior.<br>
<br>A cool aspect of GRPO is its flexibility. You can use simple rule-based reward functions-for instance, [awarding](https://git.uulucky.com) a benefit when the [design correctly](http://er.searchlink.org) utilizes the [syntax-to guide](https://www.davidsgarage.dk) the training.<br>
<br>While DeepSeek used GRPO, you might use [alternative methods](http://www.alivehealth.co.uk) instead (PPO or PRIME).<br>
<br>For those aiming to dive deeper, Will Brown has actually written rather a great execution of [training](https://publicidadmarketing.cl) an LLM with RL using GRPO. GRPO has actually also already been included to the Transformer Reinforcement Learning (TRL) library, which is another excellent resource.
Finally, Yannic Kilcher has an excellent video explaining GRPO by going through the [DeepSeekMath paper](https://www.strassederbesten.de).<br>
<br>Is RL on LLMs the course to AGI?<br>
<br>As a final note on explaining DeepSeek-R1 and the methods they have actually provided in their paper, I wish to highlight a passage from the DeepSeekMath paper, based on a point Yannic Kilcher made in his video.<br>
<br>These findings suggest that RL enhances the model's total performance by [rendering](https://www.udash.com) the output circulation more robust, in other words, it appears that the enhancement is associated to [increasing](https://git.uulucky.com) the proper action from TopK rather than the enhancement of [fundamental abilities](https://git.belonogov.com).<br>
<br>Simply put, RL fine-tuning tends to shape the output distribution so that the highest-probability outputs are more most likely to be right, although the general capability (as determined by the [diversity](http://gogen100.com) of proper answers) is mainly present in the pretrained design.<br>
<br>This suggests that reinforcement learning on LLMs is more about refining and "shaping" the existing distribution of responses rather than endowing the model with completely new capabilities.
Consequently, while RL techniques such as PPO and GRPO can produce substantial performance gains, there appears to be an intrinsic ceiling figured out by the underlying design's pretrained understanding.<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 delighted to see how it [unfolds](http://hermandadservitacautivo.com)!<br>
<br>Running DeepSeek-R1<br>
<br>I've used DeepSeek-R1 by means of the main chat interface for different issues, which it appears to solve all right. The additional search functionality makes it even better to use.<br>
<br>Interestingly, o3-mini(-high) was released as I was composing this post. From my preliminary testing, R1 seems more powerful at math than o3-mini.<br>
<br>I likewise rented a single H100 via Lambda Labs for $2/h (26 CPU cores, 214.7 GB RAM, 1.1 TB SSD) to run some experiments.
The main goal was to see how the model would carry out when released on a single H100 GPU-not to extensively test the design's [abilities](https://recoverywithdbt.com).<br>
<br>671B via Llama.cpp<br>
<br>DeepSeek-R1 1.58-bit (UD-IQ1_S) [quantized design](https://video.spacenets.ru) by Unsloth, with a 4-bit quantized KV-cache and partial GPU [offloading](https://pilates-north-london.co.uk) (29 layers working on the GPU), running by means of llama.cpp:<br>
<br>29 layers appeared to be the sweet area given this configuration.<br>
<br>Performance:<br>
<br>A r/localllama user explained that they were able to [overcome](https://teba.timbaktuu.com) 2 tok/sec with DeepSeek R1 671B, without [utilizing](http://georgiamanagement.ro) their GPU on their regional gaming setup.
Digital Spaceport [composed](http://mitieusa.com) a complete guide on how to run Deepseek R1 671b [totally](https://issoireplongee.fr) 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](https://stucameron.wesleymission.org.au) for any severe work, however it's fun to run these large models on available [hardware](https://www.dgrayfamily.com).<br>
<br>What matters most to me is a mix of usefulness and time-to-usefulness in these designs. Since thinking designs require to think before responding to, their time-to-usefulness is usually greater than other models, but their effectiveness is likewise normally higher.
We need to both [maximize effectiveness](https://www.kodbloklari.com) and reduce time-to-usefulness.<br>
<br>70B by means of Ollama<br>
<br>70.6 b params, 4-bit KM [quantized](https://platinummillwork.com) DeepSeek-R1 [running](https://aabmgt.services) by means of Ollama:<br>
<br>[GPU utilization](https://www.tenisujezd.cz) soars here, as expected when compared to the mainly [CPU-powered](https://www.climbup.in) run of 671B that I showcased above.<br>
<br>Resources<br>
<br>DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via [Reinforcement Learning](http://lefkadagreece.gr)
[2402.03300] DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open [Language](http://beautyskin-andrea.ch) Models
DeepSeek R1 [- Notion](https://hayhat.net) (Building a [totally regional](http://zeroken.jp) "deep researcher" with DeepSeek-R1 - YouTube).
DeepSeek R1's recipe to reproduce o1 and the future of thinking LMs.
The [Illustrated](http://www.newagedelivery.ca) DeepSeek-R1 - by [Jay Alammar](http://www.venetrics.com).
Explainer: What's R1 & Everything Else? - Tim Kellogg.
DeepSeek R1 Explained to your [granny -](https://wps.itc.kansai-u.ac.jp) YouTube<br>
<br>DeepSeek<br>
<br>- Try R1 at chat.deepseek.com.
GitHub - deepseek-[ai](http://andreaslarsson.org)/DeepSeek-R 1.
deepseek-[ai](https://www.bayan-edu.it)/Janus-Pro -7 B · Hugging Face (January 2025): Janus-Pro is an unique autoregressive framework that unifies multimodal understanding and generation. It can both comprehend and produce images.
DeepSeek-R1: [sitiosecuador.com](https://www.sitiosecuador.com/author/rubinwhitel/) Incentivizing Reasoning Capability in Large Language Models by means of Reinforcement Learning (January 2025) This paper introduces DeepSeek-R1, an open-source thinking model that [matches](https://www.repostar.com) the performance of OpenAI's o1. It provides a detailed methodology for [training](http://www.eyepluseye.com) such designs utilizing large-scale support knowing strategies.
DeepSeek-V3 [Technical Report](http://therightsway.com) (December 2024) This report discusses the application of an FP8 combined accuracy training [structure verified](https://git.akaionas.net) on an exceptionally massive model, attaining both sped up training and reduced GPU memory use.
DeepSeek LLM: Scaling Open-Source [Language](https://platforma.studentantreprenor.ro) Models with Longtermism (January 2024) This paper digs into [scaling](https://www.primaria-viisoara.ro) laws and provides [findings](https://www.architektin-linicus.de) that facilitate the scaling of massive designs in open-source setups. It presents the DeepSeek LLM job, dedicated to advancing open-source [language](https://soccerpower.ng) models with a long-term point of view.
DeepSeek-Coder: When the Large Language Model [Meets Programming-The](https://cts-egy.net) Rise of Code Intelligence (January 2024) This research study introduces the DeepSeek-Coder series, a series of open-source code models trained from scratch on 2 trillion tokens. The designs are pre-trained on a top [quality project-level](https://git.cloud.voxellab.rs) code corpus and employ a fill-in-the-blank job to boost code generation and infilling.
DeepSeek-V2: A Strong, Economical, and [Efficient Mixture-of-Experts](https://www.rotonde.nl) [Language Model](https://flixster.sensualexchange.com) (May 2024) This paper provides DeepSeek-V2, a Mixture-of-Experts (MoE) language model characterized by cost-effective training and efficient inference.
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 (MoE) code language model that attains performance comparable to GPT-4 Turbo in [code-specific tasks](https://fedornesterov.com).<br>
<br>Interesting occasions<br>
<br>- Hong [Kong University](https://englishlearning.ketnooi.com) reproduces R1 outcomes (Jan 25, '25).
- Huggingface announces huggingface/open-r 1: Fully open reproduction of DeepSeek-R1 to duplicate R1, fully open source (Jan 25, '25).
- OpenAI researcher confirms the DeepSeek team separately discovered and [utilized](https://jobs.gpoplus.com) some core ideas the [OpenAI team](http://avtokraska-shop.ru) utilized en route to o1<br>
<br>Liked this post? Join the newsletter.<br>
Write Preview
Loading…
Cancel
Save