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https://www.zerohedge.com/political/deepseek-ai-app-demonstrates-pro-ccp-bias-influence

lol what a shocker right there. a communist "AI" showing commie proclivities or shilling for the ccp. i'm pretty shocked i tell you /s

https://cordcuttersnews.com/spectrum-cuts-over-250-customer-service-jobs-at-one-of-its-call-centers/

more pajeets or AI for work?

🚨🚨 Deepeek-R1 !! big 3 day tech crash!? 🚨🚨 short NVDA for 5 days at market open monday for $$$

hold TSM though https://finance.yahoo.com/quote/TSM/

big 3 day tech crash?:
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https://www.zerohedge.com/news/2025-01-25/sixteen-trillion-dollar-question

omg!!!


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Deepeek-R1

free untrained MODELS at all sizes from 1.5B up to massive 671B.

https://github.com/deepseek-ai/DeepSeek-R1?tab=readme-ov-file#2-model-summary

- DeepSeek-R1-Distill-Qwen-1.5B
- DeepSeek-R1-Distill-Qwen-7B
- DeepSeek-R1-Distill-Llama-8B
- DeepSeek-R1-Distill-Qwen-14B
- DeepSeek-R1-Distill-Qwen-32B (requant to 5 bit for 24GB VRAM 4090, sweet!)
- DeepSeek-R1-Distill-Llama-70B [has some censorship] https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B
- DeepSeek-R1 671B !!!!! : https://huggingface.co/deepseek-ai/DeepSeek-R1


All free! 99.999% uncensored. fastest, best benchmarks!!!










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https://github.com/deepseek-ai/DeepSeek-V3?tab=readme-ov-file#6-how-to-run-locally

big thread :
https://news.ycombinator.com/item?id=42768072


deepseek v3 cost $5.57 million only , pretraining 2,664,000 h800 hours using 8-bit pretraining

no censorship in 32B verion

Run DeepSeek R1 with Ollama and Cline in VScode - 100% Local Solution:
https://www.youtube.com/watch?v=oeBDn6vclz0

How to Run DeepSeek R1 Locally (Better Than OpenAI o1) Tutorial:
https://www.youtube.com/watch?v=LJMqT-FZOX


This open source AI crushes everything - DeepSeek R1:
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https://www.youtube.com/watch?v=xCQXyZkMsbs


How to Run DeepSeek-R1 Locally | The FREE Open-Source Reasoning AI;
https://www.youtube.com/watch?v=rzMEieMXYFA


Install and Run Locally DeepSeek-R1 AI Model on Windows:
https://www.youtube.com/watch?v=1zsK0U33hN4

Chinese AI platform DeepSeek overtakes ChatGPT on Apple's app download rankings in the US
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https://arxiv.org/abs/2412.19437

DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models. Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training. In addition, its training process is remarkably stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks.


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DeepSeek has 50,000 Nvidia H100 gpus [probably gimped 4090d versions]

DeepSeek has $450 million in pretraining GPU cards, 200 grunt employees, just 17 ai experts

DeepSeek has fully crushed the LLM market, 50 times cheaper and far better!

ai pings : @QuestionEverything , @x0x7 , @Monica , @MasterSuppressionTechnique , @prototype , @observation1 , @taoV , @SecretHitler, @Master_Foo, @Crackinjokes, @Sheitstrom, @FuckShitJesus, @wyrmblut

We have a true open source LLM with o1 performance (or better?). Trained on 671B params with reinforcement learning. Cost per million tokens is ~$2, compared to ~$60 from OpenAI. Mixture-of-experts MOE architecture (similar to model orchestration?).

I still need to read the white paper but what should I be looking for with this? LLM deployment/dev becomes super cheap, all the major US companies need to cut prices to compete, people can run a much improved LLM on their home PC, I really just see this as cutting costs, increasing competition and a good thing for people, despite the losses for corporate America tech(currently in a tail-spin). Security is a massive concern being it is a Chinese product so we will need to make a more secure version that can be trusted. Also, the compute power would be largely distributed meaning infrastructure strain would be significantly less than an enterprise focused ecosystem.

Trying to get my head around this, any ideas? Boomers, no one gives a shit about how you are retired and hate new technology, we know.

I'm out of touch with smart phones these days. Curious what the average is. I get about 3 days on a single charge with a moto g power.