6.11.2022 dávali v televízii starý slovenský film Zemianska česť, na ktorý sme takmer zabudli. Nevieme, či ho v telke dávali v súvislosti s práve ubehnuvšími voľbami alebo hroziacimi predčasnými voľbami do parlamentu, ale či už tak alebo onak, načasovanie nemohlo byť lepšie. Ide totiž o satiru na predvolebnú frašku sfilmovanú podľa románu Jána Kalinčiaka Reštavrácia.
Napriek tomu, že román pochádza z roku 1860, je až neuveriteľné, ako nadčasovo je napísaný, pretože charaktery a charakteristiky postavičiek, ako aj ich správanie, možno napasovať na všetky politické figúrky, ktoré sme u nás doposiaľ mali a stále máme. No a rovnako aj na voličov, ktorí tieto politické figúrky volia.
Repríza televíznej klasiky nás priviedla k zamysleniu, kde sú korene takého správania a takých charakterov. Vzhľadom na dobu, kedy román vznikol, je zrejmé, že s tým určite nemá nič spoločné obdobie socializmu, na ktoré sa často zvaľuje všetko zlé, čo sa u nás dialo a deje. Takto sme sa správali už dávnom pred socializmom. Je to teda v nás, v národe, v našich génoch, že sme takým falošným, úplatným, bezzásadovým a bezcharakterným národom? Necháme sa kúpiť za klobásu či poldeci pálenky?
Odkaz románu do dnešnej doby
Dá sa teda pobaviť a zasmiať aj na takom starom filme? Určite áno, najmä keď si v jeho hrdinoch predstavíme naše súčasné politické postavičky. Realita dnešných dní však, bohužiaľ, nie je ani trochu zábavná, takže ide skôr o smiech cez slzy.
Ako sa to vo filme vlastne skončilo? Všetci sa pobratali, funkcie si podelili a obidve bandy vládli v podstate spolu. Nikoho ani vtedy nezaujímalo, ako je na tom krajina, ako sa vodí obyčajnému ľudu. Za všetko hovoria posledné slová filmu: „Nuž, ľa! Čo sa statočný zeman natrápi, kým vyjde na psí tridsiatok!“. A čo tak parafráza na súčasnú dobu: „Nuž, ľa! Čo sa statočná Slovač natrápi, kým vyjde na psí tridsiatok!“.
Nuž, od toho asi už nemáme ďaleko, keďže sa od Kalinčiakových čias vlastne nič nezmenilo. Ale môže sa, stačí iba odstaviť podarených politikov a hlavne volebný systém, ktorý im šafárenie umožňuje. Práve preto je tu tento portál, aby poukázal, kde je podstata tohto zla. Kým si neuvedomíme tieto fundamentálne okolnosti, nie je možné ani hľadať riešenia.
Román Reštavrácia a film Zemianska česť tak ostávajú nielen skvelou komédiou, ale aj mementom, že tváre sa síce menia, no politické taktiky ostávajú zhruba rovnaké. Voľby boli, sú a vždy budú iba fraškou pre verejnosť, zorganizovanou ziskuchtivými a mocibažnými indivíduami.
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DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support knowing (RL) to enhance reasoning capability. DeepSeek-R1 attains outcomes on par with OpenAI’s o1 model on a number of benchmarks, consisting of MATH-500 and SWE-bench.
DeepSeek-R1 is based upon DeepSeek-V3, a mixture of experts (MoE) design recently open-sourced by DeepSeek. This base design is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented variation of RL. The research team also performed understanding distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched several variations of each; these designs surpass bigger models, consisting of GPT-4, on math and coding benchmarks.
[DeepSeek-R1 is] the first step toward enhancing language model reasoning capabilities utilizing pure reinforcement learning (RL). Our goal is to explore the potential of LLMs to establish thinking abilities without any monitored information, focusing on their self-evolution through a pure RL process…DeepSeek-R1 … excels in a vast array of tasks, including innovative writing, basic question answering, modifying, summarization, and more. Additionally, DeepSeek-R1 demonstrates impressive performance on jobs requiring long-context understanding, considerably outperforming DeepSeek-V3 on long-context standards.
To establish the design, DeepSeek started with DeepSeek-V3 as a base. They first attempted fine-tuning it just with RL, and without any monitored fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have actually also released. This design shows strong thinking performance, however“ effective thinking behaviors, it deals with a number of issues. For example, DeepSeek-R1-Zero fights with challenges like poor readability and language blending.“
To address this, the team utilized a brief phase of SFT to avoid the „cold start“ problem of RL. They gathered numerous thousand examples of chain-of-thought thinking to use in SFT of DeepSeek-V3 before running RL. After the RL procedure converged, they then gathered more SFT information utilizing rejection sampling, resulting in a dataset of 800k samples. This dataset was utilized for more fine-tuning and to produce the distilled models from Llama and Qwen.
DeepSeek examined their design on a variety of reasoning, mathematics, and coding benchmarks and compared it to other models, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 exceeded all of them on numerous of the standards, consisting of AIME 2024 and MATH-500.
DeepSeek-R1 Performance. Image Source: DeepSeek-R1 Technical Report
Within a few days of its release, the LMArena revealed that DeepSeek-R1 was ranked # 3 general in the arena and # 1 in coding and math. It was likewise tied for # 1 with o1 in „Hard Prompt with Style Control“ category.
Django framework co-creator Simon Willison discussed his try outs one of the DeepSeek distilled Llama models on his blog:
Each response starts with a … pseudo-XML tag containing the chain of thought used to assist produce the response. [Given the timely] „a joke about a pelican and a walrus who run a tea room together“ … It then believed for 20 paragraphs before outputting the joke! … [T] he joke is horrible. But the process of getting there was such a fascinating insight into how these brand-new models work.
Andrew Ng’s newsletter The Batch wrote about DeepSeek-R1:
DeepSeek is quickly becoming a strong home builder of open models. Not only are these models excellent entertainers, but their license allows use of their outputs for distillation, potentially pushing forward the state of the art for language models (and multimodal models) of all sizes.
The DeepSeek-R1 designs are available on HuggingFace.
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