AGILLMMark-2

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This is a simple mistake. I call them but I try to run my own code from something like [ 13/10/2025 09:45 Chillia trained now on lower end of it ie 20, may go up to 25.6 (the higher end law) about to do instruct-chat turning now or very soon note to self in mark 3 use both deepseek and qwen tokinzer/vocab as one unified system, also mark 3 improve ali and miminal mamba addition. (main) [email protected]:/workspace$ python 5ap1.py infer
--mode ar
--ckpt ckpts_joint/step01527499.pt \ (this is acutally checkpoint 26XXX....pt) --preset smallx2
--prompt "Write exactly three concise bullet points explaining the role of attention in transformers. Use a leading dash for each point. No intro, no outro, no numbering."
--max_new 120
--temperature 0.2
--top_p 0.9
--repetition_penalty 1.05
--penalty_last_n 128
--no_repeat_ngram_size 3 /workspace/5ap1.py:101: FutureWarning: You are using torch.load with weights_only=False (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for weights_only will be flipped to True. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via torch.serialization.add_safe_globals. We recommend you start setting weights_only=True for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. return torch.load(path, map_location="cpu") Write exactly three concise bullet points explaining the role of attention in transformers. Use a leading dash for each point. No intro, no outro, no numbering. The first step is to find the best possible solutions for the system. The second step is the most important step. The third step is a simple step. It is the first step. If you are looking for a new tool, you can find the right solution. The goal is to get the best results. The result is the second step. You can find a way to get a new model. The other step is that the model is the best method to solve the problem. The more the model will be the first part of the problem, the more the better the model. You will find the [120 tok in 2.15s] signs or replying to questions (main) [email protected]:/workspace$

(main) [email protected]:/workspace$ python 5ap1.py infer \ 21/09/2025 23:39:17 GB --mode ar
--ckpt ckpts_joint/step02326865.pt
--preset smallx2
--prompt "List ten ways multi head attention improves transformer models in clear bullet points without full stops"
--max_new 800
--temperature 0.8
--top_p 0.95
--repetition_penalty 1.1
--penalty_last_n 128
--no_repeat_ngram_size 3 /workspace/5ap1.py:101: FutureWarning: You are using torch.load with weights_only=False (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for weights_only will be flipped to True. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via torch.serialization.add_safe_globals. We recommend you start setting weights_only=True for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. return torch.load(path, map_location="cpu") List ten ways multi head attention improves transformer models in clear bullet points without full stops from the lens. The game also adds a problem with the matchster x but they remain closed behind. As are the games, there are still no users connected to the graphics. Even if you don't know about them, it was an unanceiling kitchen anymore. It's a couple of years ago. It makes a lot of sound that quite comes into focus on the video.Ahmed Azamoub, zie ma, wveda.

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Hockey Nabodad Rye Radiu Tobie Cyrac Furn Michael Simo Boul Nemba Jim Sha Kete Chun Tázamoub Litie Capry Smeane Star Wars Apbon Aau Catherine Gresc Deb Robert Deaf Cocos Gregr Guo TamilA Newton P. Bell In 1886, United States Congress passed legislation banning activities by university administration. The department estimated that America was built at the beginning of 1.Asserting such a policy to top college students in the 1炸 education system, considering long distance graduation laws such as Georgia, New York, North Dakota, Rhode Island, Vermont, Wisconsin, and Philadelphia. So until 1.parts 2015 when its amendment was passed in 1789, the department kept the administrative rule that the department's supposed program would not require that its facilities were intended to "Encourians." The department had to make certain that its facility would likely be designed to install the board or maintain the existing facility. It was the program meant to complete control of that facility in Spa Industry Development Corporation's facilities andлtyselfis brought a new facility to the University of Minnesota and to Boston College. Unlike the items in which the institution was available, the community devoted itself to operating a little more, much more than one million dollars. In 2 seamless hearings, Newton P nal of APL Regulator helped to clean out those foods that kept "a dumb" food. Then as the department has been waiting for the approval process to be held and planned to buy food until the return to the campus after that time. But this is not the case. What I'm going to say about Bill Clinton's policy making? "What other than a former college professor who has so long been concerned is the fact that the population be called," said PEmergency Al-Air, who served as president of the Office of Public Health Program. The Department is required to distribute every open plan that local laws would be redesigned from providing access to the system. It's really nice to know who it is. One of those companies that people say is their organization, and their use of the right things. Those organizations also do a lot more than they believe it is appropriate for the public to keep the resources clean. Now it's important to avoid the personal, commercial, shopping, management, and policies that serve as a platform for fixing the environment. Also, House Speaker Bob Moore was the policy has been dropped, with the administration supposed to impose legislation on its own neighborhoods, so the oversight of the program's system has not been done as it depends on the ability to effectively provide a well-deserved welcome while the competition goes further. It won't have long-term effects, nor issues like the fact about those institutions that break a case for the former college administrators. But then the problem was that the program is essentially: The city impacts of the city's food supply and use of food goods in the city have [800 tok in 12.96s] (main) [email protected]:/workspace$ tokenizer/vocab used at time of training 20/09/2025 23:15:05 GB Time Windows PowerShell Copyright (C) Microsoft Corporation. All rights reserved.

Install the latest PowerShell for new features and improvements! https://aka.ms/PSWindows

Loading personal and system profiles took 1393ms. PS C:\Users\Scott\Downloads> python bi.py [pin] downloading tokenizer files from Qwen/Qwen3-235B-A22B-Thinking-2507@main README.md: 18.3kB [00:00, 22.1MB/s] | 0/5 [00:00<?, ?it/s] Fetching 5 files: 100%|██████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 19.27it/s] [pin] pinned to C:\Users\Scott\Downloads\vendor\tokenizer { "name": "Qwen/Qwen3-235B-A22B-Thinking-2507", "revision": "main", "resolved_commit": "6cbffae6d8e28b986a6b17bd36f42f9fa0f1f0a5", "files": { "added_tokens.json": "sha256:79f6ec6fcc423d3a82bfac8b9033b1daac7b7bce06a5f5f441637b480cc605de", "chat_template.jinja": "sha256:b4662678887c9457be690c5966195b060975b6ab0d6dba06eb7d7c6c3e82c9ad", "merges.txt": "sha256:8831e4f1a044471340f7c0a83d7bd71306a5b867e95fd870f74d0c5308a904d5", "special_tokens_map.json": "sha256:57255613bbe23c9497211ca68561ff429a51e871dbaf5a59998fa4c8f7fe168a", "tokenizer.json": "sha256:aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4", "tokenizer_config.json": "sha256:e1ff43043fd7fbe07a7656d301d3c59352984be693c69c4becd4b670f52a374b", "vocab.json": "sha256:ca10d7e9fb3ed18575dd1e277a2579c16d108e32f27439684afa0e10b1440910" }, "special_ids": { "pad": 151643, "eos": 151645, "bos": null, "unk": null, "mask": null }, "normalize": { "lower": false }, "vocab_size": 151643 } PS C:\Users\Scott\Downloads>

192411GB 16092025 (main) [email protected]:/workspace$ python 5ap.py infer
--mode ar
--ckpt ckpts_joint/step01560000.pt
--preset smallx2
--prompt "Explain the role of attention in transformers in 3 short bullet points."
--max_new 480
--temperature 0.7 --top_p 0.9
--repetition_penalty 1.2
--presence_penalty 0.6
--frequency_penalty 0.2
--penalty_last_n 128
--no_repeat_ngram_size 3

/workspace/5ap.py:99: FutureWarning: You are using torch.load with weights_only=False (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for weights_only will be flipped to True. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via torch.serialization.add_safe_globals. We recommend you start setting weights_only=True for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. return torch.load(path, map_location="cpu") Explain the role of attention in transformers in 3 short bullet points. The number of large numbers is at 125 mm/240 for use, but the number of small numbers can be as high as about 90% and smaller. The other three types of heavy-cap-line sites are only available in the past, but this has not been a concern for many people who have had low-lying environments or do well. In addition to the fact that these were efficient enough to get used, they also need to take advantage of their abilities and skills. The work was supported by certified high-performance aircraft with improved performance from various sources. It was a good fit to be able to reduce pressure on pilots' speed. The speed up began to increase exponentially at around 135 mm in length and increased speed over time. The average between engine engines was 40:20-150 mm. This gives a lot of flexibility and stability and precision but it doesn't require more weight and weight while its ability to fully operate. It is important for all small groups that are fairly large than the ground regions and beyond have zero impact on the number. For example, this takes about 80% of the most powerful planes in the world. The final plan for both aircraft made its way into one way through five different categories: it will be easy to use, which will help you with your skill levels, or whether you should not stay at any level, and can also take advantage out from the propellers. The first step towards getting used to make a switch onto the ground area as well as a gear like a rod or something else. The result was not a concern about how much power would operate. Instead of choosing between small groups and using smaller sizes of natural areas that have been produced, they should still need to continue a better understanding of the competition within large groups. In fact, the number on top is 20 years (136 mm/450) than in 98% with an average 70%. This will make the best decision for most aircraft equipped with eight different types: it's the second set that has two different kinds: one size may be around 26-30 times per year. The last step towards finding a key point at least in many places, so we can also consider how much more space would take. The third set of small planes can be upgraded [480 tok in 7.76s] (main) [email protected]:/workspace$

16:34:31 07/09/2025 UK time Log latest checkpoint about 10 days of 2 months (it must follow Chinllia law before you get AGI intellgence (this is absoutly the min amount u can do) (trust me i've tried lower) ie for every 1 parameter-neuron there 20 tokens+ this one does 1 to 25 mark 1 (https://huggingface.co/MarxistLeninist/AGILLMMark-1) did 1 to 20 and was smaller) in (main) [email protected]:/workspace$ CUDA_VISIBLE_DEVICES="" python3 5.py infer
--mode ar
--ckpt "/workspace/ckpts_run_1024/step00539523.pt"
--prompt "Physicalism is True"
--max_new 200
--temperature 0.3 Physicalism is True, the most important thing you can see the same thing you want to get to the best of the time. The first thing is that you can see the best thing you can get to the best of your own. The first thing you can find yourself, and you can get the best way to get the best of your own. You can also make your own. You can make sure you want to make sure you want to make sure you want to get your own. You will be able to make a great deal. You will be able to make sure you want to make sure you want to make sure you want to make your own decisions. You can also be able to keep your own. You can get a little more and take your own, and you can help you get a great deal with your own. You will have to make sure you are going to be able to make sure you want to make a better and you want to take your own. You can also be able to get a [200 tok in 12.10s] [06:55] model slowing getting better

PRO-AI/AGI/ASI Marxist-Leninist

AI — Yesterday at 09:10 21%|█████████████▎ | 1939238912/9142690425 [310:36:22<1232:13:42, 1623.85tok/s, accum=4, batch=1, block=1024, updates=440737/2199394] ; on a 1x Titan RTX VRAM at about 15.5 TFLOPS 23.6/24.0 GB using vast.ai

switched too Qwen 3 235B A22B Thinking 2507 qwen3-235b-a22b-thinking-2507 for the vocab tokenizer as it currently the smartest AGI LLM that is open source; which make it incompitable with Mark1 as it uses deepseek-r1-0528 token/vocab Activated conda/uv virtual environment at /venv/main (main) [email protected]:/workspace$ python3 5p10.py train --preset small --amp --x2 --fresh
--block 1024
--save_dir /workspace/ckpts_qwen3_small_x2_1024
--save_every_sec 259200 tokenizer_config.json: 10.8kB [00:00, 31.5MB/s] vocab.json: 2.78MB [00:00, 22.9MB/s] merges.txt: 1.67MB [00:00, 26.0MB/s] tokenizer.json: 7.03MB [00:00, 39.6MB/s] [auto-steps] 3,229,687 training steps (@ 1024 tokens/step) Resolving data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 59166/59166 [00:22<00:00, 2673.56it/s] Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 31428/31428 [00:00<00:00, 259629.53it/s] Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 31411/31411 [00:00<00:00, 242792.61it/

Note Mark 1 is a base AGI LLM model it is not an Instruct model; Mark 2 will get an Instruct version ie it will do classic chat structure so program can easily parse it into ChatGPT UI we are all familar with; Mark 1 also had absoutle postional encoding Mark 2 is relative encoding this just means that rather then being locked into say X token context window you trained it on relative postional encoding allow the model to be trained on X content window but say you trained it on 1024 absoutle postonal encoding can only produce tokens of that amount (you could try force but it would collaspe pretty fast out side of that) relative posistional encoding allows you to train on 1024 block and it be able to generate and learn strucutres bigger then that so it say produce 2048 tokens instead of only be limited to 1024.