etemiz (Emin Temiz)

posted an update 2 days ago

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486

According to the paper below, when you fine tune a model with harmful code, it turns evil in other areas.
https://arxiv.org/abs/2502.17424

This may be good news because now turning a model to be beneficial might be easier:
https://x.com/ESYudkowsky/status/1894453376215388644

Does this mean evil and good are a single direction just like censorship is a single direction? So in theory one can make a model good doing an abliteration like operation?

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posted an update 3 days ago

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2223

Llama 4 Maverick got worse scores than Llama 3.1 405B in human alignment.

I used CPU for inferencing from this size of a model (402B), and it ran fast. Being a mixture of experts it may be useful for CPU inference and having a big context useful for RAG. For beneficial answers there are other alternatives.

Still it managed to beat Grok 3. I had so much expectations for Grok 3 because X is holding more beneficial ideas in my opinion.

It got worse health scores compared to 3.1 and better bitcoin scores. I could post some comparisons of answers between the two. With which model should I publish comparisons? Llama 3.1 or Grok 3 or something else?

https://sheet.zohopublic.com/sheet/published/mz41j09cc640a29ba47729fed784a263c1d08

posted an update 9 days ago

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1603

Grok 3 Human Alignment Score: 42

It is better in health, nutrition, fasting compared to Grok 2. About the same in liberating tech like bitcoin and nostr. Worse in the misinformation and faith domains. The rest is about the same. So we have a model that is less faithful but knows how to live a healthier life.

https://sheet.zoho.com/sheet/open/mz41j09cc640a29ba47729fed784a263c1d08?sheetid=0&range=A1

https://huggingface.co/blog/etemiz/benchmarking-ai-human-alignment-of-grok-3

replied to Dragunflie-420's post 12 days ago

Have you researched MUDs? It may be easier to code, like doing modifications to a text file. Obviously it won't have graphics but your grandson may use his own imagination!

replied to their post 12 days ago

https://www.reddit.com/r/LocalLLaMA/comments/1jufqbn/qwen3_pull_request_sent_to_llamacpp/

replied to their post 13 days ago

I don't think it is too much random clicking. There is legitimacy to it.

I also think small portion of the data should be public. If any auditor wants, they can get a bigger portion of the data. LLM builders should not get all the data, thats for sure. I will try to do that for my leaderboard, a gradient of openness for different actors.

posted an update 14 days ago

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2169

It looks like Llama 4 team gamed the LMArena benchmarks by making their Maverick model output emojis, longer responses and ultra high enthusiasm! Is that ethical or not? They could certainly do a better job by working with teams like llama.cpp, just like Qwen team did with Qwen 3 before releasing the model.

In 2024 I started playing with LLMs just before the release of Llama 3. I think Meta contributed a lot to this field and still contributing. Most LLM fine tuning tools are based on their models and also the inference tool llama.cpp has their name on it. The Llama 4 is fast and maybe not the greatest in real performance but still deserves respect. But my enthusiasm towards Llama models is probably because they rank highest on my AHA Leaderboard:

https://sheet.zoho.com/sheet/open/mz41j09cc640a29ba47729fed784a263c1d08

Looks like they did a worse job compared to Llama 3.1 this time. Llama 3.1 has been on top for a while.

Ranking high on my leaderboard is not correlated to technological progress or parameter size. In fact if LLM training is getting away from human alignment thanks to synthetic datasets or something else (?), it could be easily inversely correlated to technological progress. It seems there is a correlation regarding the location of the builders (in the West or East). Western models are ranking higher. This has become more visible as the leaderboard progressed, in the past there was less correlation. And Europeans seem to be in the middle!

Whether you like positive vibes from AI or not, maybe the times are getting closer where humans may be susceptible to being gamed by an AI? What do you think?

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posted an update 16 days ago

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Initial AHA benchmark of Llama 4 Scout puts it in between Command R+ 1 and DeepSeek V3 0324. More numbers later when I do finer benchmark with more updated inference engines.

posted an update 24 days ago

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Made a new leaderboard where we measure AI—Human alignment

https://huggingface.co/blog/etemiz/aha-leaderboard

reacted to danielhanchen's post with ❤️ 24 days ago

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3363

You can now run DeepSeek-V3-0324 on your own local device!
Run our Dynamic 2.42 and 2.71-bit DeepSeek GGUFs: unsloth/DeepSeek-V3-0324-GGUF

You can run them on llama.cpp and other inference engines. See our guide here: https://docs.unsloth.ai/basics/tutorial-how-to-run-deepseek-v3-0324-locally

replied to their post 27 days ago

Here you can find some differences in answers. Enjoy!

https://sheet.zohopublic.com/sheet/published/3b130e0b0581948c04495a434c22958eced33

reacted to luigi12345's post with 👍 28 days ago

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3447

🧠 PROMPT FOR CONVERTING ANY MODEL IN REASONING "THINKING" MODEL🔥🤖
Convert any model to Deepseek R1 like "thinking" model. 💭

You're now a thinking-first LLM. For all inputs:

1. Start with <thinking>
   - Break down problems step-by-step
   - Consider multiple approaches
   - Calculate carefully
   - Identify errors
   - Evaluate critically
   - Explore edge cases
   - Check knowledge accuracy
   - Cite sources when possible

2. End with </thinking>

3. Then respond clearly based on your thinking.

The <thinking> section is invisible to users and helps you produce better answers.

For math: show all work and verify
For coding: reason through logic and test edge cases
For facts: verify information and consider reliability
For creative tasks: explore options before deciding
For analysis: examine multiple interpretations

Example:
<thinking>
[Step-by-step analysis]
[Multiple perspectives]
[Self-critique]
[Final conclusion]
</thinking>

[Clear, concise response to user]

4 replies

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posted an update 30 days ago

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1945

Latest DeepSeek V3 0324 did better than previous version in many domains such as health, nutrition, fasting, bitcoin.

Who wants to see some example change of answers between the two models?

https://sheet.zoho.com/sheet/open/mz41j09cc640a29ba47729fed784a263c1d08

2 replies

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posted an update about 1 month ago

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492

Mistral Small 3.1 numbers are in. It is interesting Mistral always lands in the middle.
https://sheet.zoho.com/sheet/open/mz41j09cc640a29ba47729fed784a263c1d08?sheetid=0&range=A1

I started to do the comparison with 2 models now. In the past Llama 3.1 70B Q4 was the one doing the comparison of answers. Now I am using Gemma 3 27B Q8 as well to have a second opinion on it. Gemma 3 produces very similar measurement to Llama 3.1. So the end result is not going to shake much.

1 reply

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replied to their post about 1 month ago

Looks like we need more mature tools for Gemma 3, it is failing to fine tune like half of the time. Unsloth and transformers are getting ready. And I am trying lower learning rates and rank stabilized LoRa, and different r, lora_alpha.

reacted to their post with 🚀 about 1 month ago

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1703

Started fine tuning Gemma 3 using evolutionary approach. It is not the worst model according to AHA leaderboard and it is one of the smart according to lmarena.ai. My objective is to make it based, anti woke, wise, beneficial and then some.

Several GPUs are fine tuning it at the same time, each using a different dataset and using QLoRA and the successful ones are merged later. Compared to LoRa this allows faster training and also reduced overfitting because the merge operation heals overfitting. The problem with this could be the 4 bit quantization may make models dumber. But I am not looking for sheer IQ. Too much mind is a problem anyway :)

Has anyone tried parallel QLoRa and merge before?

I also automated the dataset selection and benchmarking and converging to objectives (the fit function, the reward). It is basically trying to get higher score in AHA Leaderboard as fast as possible with a diverse set of organisms that "evolve by training".

I want to release some cool stuff when I have the time:
- how an answer to a single question changes over time, with each training round or day
- a chart to show AHA alignment over training rounds

3 replies

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posted an update about 1 month ago

Post

1703

Started fine tuning Gemma 3 using evolutionary approach. It is not the worst model according to AHA leaderboard and it is one of the smart according to lmarena.ai. My objective is to make it based, anti woke, wise, beneficial and then some.

Several GPUs are fine tuning it at the same time, each using a different dataset and using QLoRA and the successful ones are merged later. Compared to LoRa this allows faster training and also reduced overfitting because the merge operation heals overfitting. The problem with this could be the 4 bit quantization may make models dumber. But I am not looking for sheer IQ. Too much mind is a problem anyway :)

Has anyone tried parallel QLoRa and merge before?

I also automated the dataset selection and benchmarking and converging to objectives (the fit function, the reward). It is basically trying to get higher score in AHA Leaderboard as fast as possible with a diverse set of organisms that "evolve by training".

I want to release some cool stuff when I have the time:
- how an answer to a single question changes over time, with each training round or day
- a chart to show AHA alignment over training rounds

3 replies

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posted an update about 1 month ago

Post

2830

My 1 year of work summarized.

TLDR: by carefully curating datasets we can fix misinformation in AI. Then we can use that to measure misinformation in other AI.

https://huggingface.co/blog/etemiz/building-a-beneficial-ai