subreddit
stringclasses 4
values | created_at
timestamp[ns, tz=US/Central]date 2025-04-30 18:10:44-0500
2025-10-08 18:20:55-0500
| retrieved_at
timestamp[ns, tz=US/Central]date 2025-05-01 18:22:20-0500
2025-10-08 18:21:45-0500
| type
stringclasses 2
values | text
stringlengths 1
41.6k
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int64 -79
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LocalLLaMA
| 2025-04-30T20:21:15-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
I see still no function calling.
| 7 |
mpy0g1f
|
t3_1kbvwsc
|
LocalLLaMA
| 2025-04-30T23:22:50-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Waiting for Mistral-Small 3.2 Reasoning. :)
| 8 |
mpysxfc
|
t3_1kbvwsc
|
LocalLLaMA
| 2025-04-30T23:13:59-05:00 | 2025-05-01T18:22:26.071698-05:00 |
post
|
Qwen 3 4B is the future, ladies and gentlemen
| 333 |
1kc016i
| null |
LocalLLaMA
| 2025-04-30T23:40:20-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
this is getting ridiculous with all these qwen 3 posts about a 4b model knowing how many R's are in strawberry or if 9.9 is greater than 9.11. It's ALL in the training data, we need new tests.
Edit: is it impressive? Yes, and I thank the Qwen team for all their work. I don't want to sound like this isn't still amazing
| 166 |
mpyv8iy
|
t3_1kc016i
|
LocalLLaMA
| 2025-04-30T23:29:57-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
I'm gonna put my hands on its 8B version real fast. Looks like llama3.1 has a serious open-source contender in this size.
| 80 |
mpytvgk
|
t3_1kc016i
|
LocalLLaMA
| 2025-05-01T03:41:47-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
| 27 |
mpzk7rt
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t3_1kc016i
|
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LocalLLaMA
| 2025-05-01T01:05:54-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
i run it on my iPhone 16 Pro Max and it's fast enough
| 20 |
mpz59ka
|
t3_1kc016i
|
LocalLLaMA
| 2025-05-01T03:25:27-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Now tell it it doesnt know semantic versioning
| 16 |
mpzir1i
|
t3_1kc016i
|
LocalLLaMA
| 2025-05-01T00:21:57-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Smallest model i've seen get that right, impressive
| 14 |
mpz0h2y
|
t3_1kc016i
|
LocalLLaMA
| 2025-04-30T23:27:09-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
| 8 |
mpyti6n
|
t3_1kc016i
|
|
LocalLLaMA
| 2025-05-01T03:23:37-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
| 7 |
mpzil2t
|
t3_1kc016i
|
|
LocalLLaMA
| 2025-05-01T00:20:29-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
now try smaller.
| 6 |
mpz0b07
|
t3_1kc016i
|
LocalLLaMA
| 2025-05-01T04:54:58-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Will it run on MacBook m1 pro?
| 3 |
mpzr2ri
|
t3_1kc016i
|
LocalLLaMA
| 2025-05-01T12:06:26-05:00 | 2025-05-01T18:22:26.071698-05:00 |
post
|
New TTS/ASR Model that is better that Whisper3-large with fewer paramters
| 207 |
1kcdxam
| null |
LocalLLaMA
| 2025-05-01T12:19:44-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Doesn't mention TTS on the page. Did you mean STT?
| 90 |
mq1uigs
|
t3_1kcdxam
|
LocalLLaMA
| 2025-05-01T12:26:20-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
English only unfortunately
| 53 |
mq1vwtz
|
t3_1kcdxam
|
LocalLLaMA
| 2025-05-01T12:10:30-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Char, word, and segment level timestamps.
Speaker recognition needed and this will be super useful!
Interesting how little compute they used compared to llms
| 42 |
mq1skjn
|
t3_1kcdxam
|
LocalLLaMA
| 2025-05-01T12:10:22-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
This is the most impressive part:
* 10,000 hours from human-transcribed NeMo ASR Set 3.0, including:
* LibriSpeech (960 hours)
* Fisher Corpus
* National Speech Corpus Part 1
* VCTK
* VoxPopuli (English)
* Europarl-ASR (English)
* Multilingual LibriSpeech (MLS English) – 2,000-hour subset
* Mozilla Common Voice (v7.0)
* AMI
* 110,000 hours of pseudo-labeled data from:
* YTC (YouTube-Commons) dataset\[4\]
* YODAS dataset \[5\]
* Librilight \[7\]
That mix is far more superior than Whisper's mix
| 33 |
mq1sjid
|
t3_1kcdxam
|
LocalLLaMA
| 2025-05-01T12:19:50-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Ahhh no diarization?
| 13 |
mq1ujb8
|
t3_1kcdxam
|
LocalLLaMA
| 2025-05-01T12:24:58-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
no tts, just asr. Please don't write misleading titles.
| 12 |
mq1vmfp
|
t3_1kcdxam
|
LocalLLaMA
| 2025-05-01T12:22:53-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Extremely good stuff. Very accurate transcription and punctuation. Also I put and entire soundtrack in it and it detected absolutely no dialogue.
Amazing.
| 12 |
mq1v6g7
|
t3_1kcdxam
|
LocalLLaMA
| 2025-05-01T12:58:06-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Uhhh really nice transcription performance, 0,6b params is insane for this performance... seems like NVIDIA is finally cooking for once! Only petpeeve: English only :-(
| 8 |
mq22mea
|
t3_1kcdxam
|
LocalLLaMA
| 2025-05-01T13:44:56-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Fuck this. We don’t need Nvidia trying to push a proprietary format into the space.
| 7 |
mq2c5kz
|
t3_1kcdxam
|
LocalLLaMA
| 2025-05-01T12:07:41-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
This model tops an ASR leaderboard with 1B fewer parameters than Whisper3-large: https://huggingface.co/spaces/hf-audio/open_asr_leaderboard
| 6 |
mq1rzdz
|
t3_1kcdxam
|
LocalLLaMA
| 2025-05-01T11:57:25-05:00 | 2025-05-01T18:22:26.071698-05:00 |
post
|
The models developers prefer.
Source: [https://x.com/cursor\_ai/status/1917982557070868739](https://x.com/cursor_ai/status/1917982557070868739)
| 146 |
1kcdpce
| null |
LocalLLaMA
| 2025-05-01T12:19:34-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
which one do you run locally?
| 100 |
mq1uh9c
|
t3_1kcdpce
|
LocalLLaMA
| 2025-05-01T12:22:08-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Cursor makes it difficult to run local models unless you proxy through a public IP so you're getting skewed results.
| 71 |
mq1v0x0
|
t3_1kcdpce
|
LocalLLaMA
| 2025-05-01T12:56:12-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
The models *people who use Cursor* prefer. Personally I use the Aider leaderboard.
| 25 |
mq227uf
|
t3_1kcdpce
|
LocalLLaMA
| 2025-05-01T12:32:21-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Who uses o3 on cursor? It's expensive af
| 23 |
mq1x6rj
|
t3_1kcdpce
|
LocalLLaMA
| 2025-05-01T13:37:35-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
I think Gemini 2.5 Pro is a big step into the right direction.
At first I couldn't see why people used Claude 3.5 over GPT-4o and. To me GPT-4o was better back then. Then I switched to o3-mini and R1. I think o3-mini is a little better than R1 but not significant.
Then Claude 3.7 arrived and I finally could see why people love Claude so much. It was better than anything else. But I still had some code which it was unable to fix and instead generated the same wrong code over and over again.
Not so with Gemini 2.5 Pro, to me it is able to basically code anything I want and with multiple iterations it can fix anything without repeating wrong code.
I can't even say if it can get any better. It also does not get dumb with long context, at least not to what I used it so far at a maximum of \~110k context.
(Claude 3.7 starts at \~25-40k+ to get off track a little, do not know exactly where it starts but definitely earlier than Gemini 2.5 Pro if it is at all getting dumber)
With dumber I mean that it starts to not follow your instructions as close as expected or even having syntax errors in code, like forgetting to close a bracket.
| 11 |
mq2ana5
|
t3_1kcdpce
|
LocalLLaMA
| 2025-05-01T12:59:56-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Developers or "developers"? I wonder how many of these users do not have any knowledge of programming and software development.
| 12 |
mq2308j
|
t3_1kcdpce
|
LocalLLaMA
| 2025-05-01T13:19:55-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Lol. [Cursor.ai](http://Cursor.ai) (and you) have no ducking clue. That's the point of running them locally
| 5 |
mq273a8
|
t3_1kcdpce
|
LocalLLaMA
| 2025-05-01T12:34:56-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Finally, a benchmark that matches my vibes.
| 2 |
mq1xqaa
|
t3_1kcdpce
|
LocalLLaMA
| 2025-05-01T14:25:32-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Interesting o3 is the fastest growing. I thought using it required charging outside the normal subscription.
I use Gemini 2.5 Pro primarily. Reasoning model, but super fast at generation so feels the same speed as Claude 3.7 sonnet overall.
| 2 |
mq2ke6k
|
t3_1kcdpce
|
LocalLLaMA
| 2025-05-01T15:28:26-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
I am surprised to see Claude 3.7 ranking higher than Gemini 2.5 pro given the known problem of Claude 3.7 making unnecessary changes.
I am curious how Cursor comes to this data, for example how does Cursor's 'auto selection' option affect the results here? Could it lead to data skew?
| 2 |
mq2xizf
|
t3_1kcdpce
|
LocalLLaMA
| 2025-04-30T22:04:58-05:00 | 2025-05-01T18:22:26.071698-05:00 |
post
|
New training method shows 80% efficiency gain: Recursive KL Divergence Optimization
| 140 |
1kbytzk
| null |
LocalLLaMA
| 2025-04-30T22:23:34-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
But can it be used for ongoing fine tuning?
| 23 |
mpykn4i
|
t3_1kbytzk
|
LocalLLaMA
| 2025-05-01T04:50:10-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
I don't understand a thing (most like an issue on my side), so a generic question:
Is it for LLMs or for images?
You posted here in LocalLLaMA so I guess it's for LLMs, but the notebook is using PIL and the paper uses CIFAR-10, CIFAR-100 and STL-10, which are image datasets?!
When it is for images, do you have an implementation for one of many open source trainers (kohya, SimpleTuner, ...) so that we can see how the claims perform against real world tasks?
| 11 |
mpzqlco
|
t3_1kbytzk
|
LocalLLaMA
| 2025-04-30T23:26:03-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
I put the paper inside a notebooklm for a podcast-like audio overview: https://notebooklm.google.com/notebook/6b5551ac-e51e-4b44-a828-805f5199417e/audio
| 7 |
mpytcpm
|
t3_1kbytzk
|
LocalLLaMA
| 2025-05-01T01:33:05-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
So, depending on your constraints you can train (best for finetuning it looks like) faster/cheaper/with less hw resources ? Looks promising!
| 4 |
mpz8204
|
t3_1kbytzk
|
LocalLLaMA
| 2025-05-01T05:56:52-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
GPL 3 licenced code in the paper
| 3 |
mpzxs97
|
t3_1kbytzk
|
LocalLLaMA
| 2025-05-01T06:30:47-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
This looks like a simple and solid improvement
| 2 |
mq0222t
|
t3_1kbytzk
|
LocalLLaMA
| 2025-05-01T11:56:00-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
It looks like it's an improvement for short or compute-constrained training. If I understood correctly, their method came out ahead in early training, especially the first two epochs, but was sometimes overtaken by more traditional training methods by epoch 10.
As others in the thread have pointed out, this makes me think this would be well suited to fine-tuning. Also perhaps in situations where you need to run many short training runs for shorter experiments, or when you're compute constrained, etc.
| 1 |
mq1pj7t
|
t3_1kbytzk
|
LocalLLaMA
| 2025-04-30T19:25:02-05:00 | 2025-05-01T18:22:26.071698-05:00 |
post
|
Phi 4 Reasoning
| 116 |
1kbvrgs
| null |
LocalLLaMA
| 2025-04-30T19:39:33-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
holy shit the microsoft openai partnership paid off here, phi 4 reasoning is probably the only open source model trained directly off of openai o series models
| 37 |
mpxt858
|
t3_1kbvrgs
|
LocalLLaMA
| 2025-04-30T19:28:00-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
The related blog post: [https://azure.microsoft.com/en-us/blog/one-year-of-phi-small-language-models-making-big-leaps-in-ai/](https://azure.microsoft.com/en-us/blog/one-year-of-phi-small-language-models-making-big-leaps-in-ai/)
| 18 |
mpxr8fw
|
t3_1kbvrgs
|
LocalLLaMA
| 2025-04-30T19:31:40-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Can't believe its been a year since the first Phi SLM dropped. Edge AI applications based on these SLMs would be super cool to see, and MSFT has the resources to pull it off.
| 10 |
mpxrv28
|
t3_1kbvrgs
|
LocalLLaMA
| 2025-04-30T20:27:23-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Interesting 🤔
| 3 |
mpy1h99
|
t3_1kbvrgs
|
LocalLLaMA
| 2025-05-01T06:33:31-05:00 | 2025-05-01T18:22:26.071698-05:00 |
post
|
Impressive Qwen 3 30 MoE
I work in several languages, mainly Spanish,Dutch,German and English and I am perplexed by the translations of Qwen 3 30 MoE! So good and accurate! Have even been chatting in a regional Spanish dialect for fun, not normal! This is scifi🤩
| 112 |
1kc6hgn
| null |
LocalLLaMA
| 2025-05-01T07:37:24-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
The MoE is great, and in terms of CPU-only inference, it's the best you can get at reasonable speeds. However, its language capabilities still have some slips here and there.
At least when it comes to the German language, it sometimes makes gender mistakes or uses phrasing that still sounds a bit too English to me as a native speaker.
That's nothing terribly bad or altering the meaning, but it still needs to be double-checked.
| 29 |
mq0bpdz
|
t3_1kc6hgn
|
LocalLLaMA
| 2025-05-01T06:50:10-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
That's nice! Just a heads up, while models may seem good on the surface when using other languages, the intelligence often becomes degraded, severely even sometimes.
Try to do some more complex, logical tasks in other languages, and see if its intelligence has been crippled or not.
| 19 |
mq04qap
|
t3_1kc6hgn
|
LocalLLaMA
| 2025-05-01T09:43:39-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Don't get fooled over, it's just benchmaxxing! /s they just benchmaxxdd every use case a user can think of and now we think the model is just good. Duh.
| 11 |
mq0ymlb
|
t3_1kc6hgn
|
LocalLLaMA
| 2025-05-01T09:17:42-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Yeah even q2\_K\_L quant is speaking Polish amazingly well, even does almost high precision multiplication. I have potato computer with AVX2 with 16 GB ram, and have good 7 t/s speed still. I asked it questions that I would ask Gemini and it answered very well. I must say it's must have model for everyone, there is no excuse, only need plenty of ram with avx2 cpu and 10-ish gb of space and you have fully functional LLM. Tried 4B Q\_4\_K\_M model with default llama.cpp settings and it was worse with my questions, not mentioning it was slower.
| 7 |
mq0tg2d
|
t3_1kc6hgn
|
LocalLLaMA
| 2025-05-01T09:21:50-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
that's unsurprise given the diversity of training dataset
| 2 |
mq0u9m8
|
t3_1kc6hgn
|
LocalLLaMA
| 2025-05-01T16:11:44-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Romanian language I am happy it was added to Qwen3, but it needs to be improved. Gemma 27b and even 12b are better in this regard than qwen 3 30 and qwen3 32b, but I am happy Qwen 3 improved a lot in multilingual aspect.
| 2 |
mq36ezo
|
t3_1kc6hgn
|
LocalLLaMA
| 2025-05-01T12:31:44-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Is it just me that it's getting random chinese tokens in the output from this model? Serving it via ollama.
| 1 |
mq1x25p
|
t3_1kc6hgn
|
LocalLLaMA
| 2025-05-01T12:46:58-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Which dialect? Pretty please?
| 1 |
mq209rg
|
t3_1kc6hgn
|
LocalLLaMA
| 2025-05-01T07:11:34-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Crying in 3080ti
| -5 |
mq07tbe
|
t3_1kc6hgn
|
LocalLLaMA
| 2025-05-01T14:33:44-05:00 | 2025-05-01T18:22:26.071698-05:00 |
post
|
Anthropic claims chips are smuggled as prosthetic baby bumps
Anthropic wants tighter chip control and less competition for frontier model building. Chip control on you but not me. Imagine that we won’t have as good DeepSeek models and Qwen models.
https://www.cnbc.com/amp/2025/05/01/nvidia-and-anthropic-clash-over-us-ai-chip-restrictions-on-china.html
| 102 |
1kchgyo
| null |
LocalLLaMA
| 2025-05-01T14:47:11-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Yeah check out his blog. Dario has a massive ego
\`\`\`
[https://www.darioamodei.com/post/on-deepseek-and-export-controls](https://www.darioamodei.com/post/on-deepseek-and-export-controls)
\`\`\`
| 73 |
mq2owmp
|
t3_1kchgyo
|
LocalLLaMA
| 2025-05-01T15:02:46-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Dario's is a loser kid and doesn't want any kids to have toys so they be as lame as him. Sure crying to mama state will make him happier, but mama state will soon ignore him because this shit gets old quickly.
| 58 |
mq2s62u
|
t3_1kchgyo
|
LocalLLaMA
| 2025-05-01T15:08:26-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Not allowing competition is small PP energy.
| 47 |
mq2tc89
|
t3_1kchgyo
|
LocalLLaMA
| 2025-05-01T14:56:50-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Why are they going so far out of their way to fuck over the open source community? You have all the top talent from OpenAI, billions in funding, and compute. There is no need to do this. This route is going to have a further negative impact on the US economy. This just leads China to produce its own euv machines, fabs, and chips.
| 41 |
mq2qxog
|
t3_1kchgyo
|
LocalLLaMA
| 2025-05-01T14:58:45-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Dario can eat shit. His company powers the genocide in Palestine.
| 35 |
mq2rc7a
|
t3_1kchgyo
|
LocalLLaMA
| 2025-05-01T15:30:03-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
This is why i seriously dislike anthropic. Their models are good, but i refuse to use them as this would mean supporting their consumer unfriendly practices.
Of course we should not be entitled to open-weight models. Companies need to be profitable and I understand that. The fact that deepseek and qwen are making millions of dollars worth of trained models open weight is more than we deserve and I am very grateful for that.
The fact that anthropic is trying to stop this (by any means necessary) is just bad taste. They have every possible advantage: they have the talent, the gpus, the money, and they get to keep their secrets while profiting from open source science. And still….
I’m glad that deepseek V3.1 and gemini 2.5 pro outclass 3.5/3.7 sonnet and their reasoning model respectively, as they do any possible use case id have for sonnet but do it better.
| 32 |
mq2xv9w
|
t3_1kchgyo
|
LocalLLaMA
| 2025-05-01T15:10:25-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
I suspect Chinese local GPUs will be competitive with NVidia before the AWS Trainum stack Anthropic relies on is good enough for them not to need to constantly throttle their users.
| 28 |
mq2tr21
|
t3_1kchgyo
|
LocalLLaMA
| 2025-05-01T15:37:16-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
If there’s any way I can help smuggle chips into China, I’ll do it for free. The world needs it.
| 21 |
mq2zd57
|
t3_1kchgyo
|
LocalLLaMA
| 2025-05-01T15:45:07-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Claude is one of my favorite models.
But Dario is very quickly becoming my least favorite CEO
| 21 |
mq30zum
|
t3_1kchgyo
|
LocalLLaMA
| 2025-05-01T15:32:05-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Dario is a loser
| 20 |
mq2yahl
|
t3_1kchgyo
|
LocalLLaMA
| 2025-04-30T21:22:03-05:00 | 2025-05-01T18:22:26.071698-05:00 |
post
|
Shuttle-3.5 (Qwen3 32b Finetune)
We are excited to introduce Shuttle-3.5, a fine-tuned version of [Qwen3 32b](https://huggingface.co/Qwen/Qwen3-32B), emulating the writing style of Claude 3 models and thoroughly trained on role-playing data.
[https://huggingface.co/shuttleai/shuttle-3.5](https://huggingface.co/shuttleai/shuttle-3.5)
| 99 |
1kby1en
| null |
LocalLLaMA
| 2025-04-30T21:25:30-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Dude. How are you so fast
Edit: Can you provide link to your model?
| 27 |
mpybgbk
|
t3_1kby1en
|
LocalLLaMA
| 2025-04-30T21:34:36-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
gguf when?
| 8 |
mpycz4r
|
t3_1kby1en
|
LocalLLaMA
| 2025-05-01T08:33:39-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Not to sound greedy, but 32B is a bit too much for my potato, could you please consider 30B A3B version? or the 14B?
| 6 |
mq0l64y
|
t3_1kby1en
|
LocalLLaMA
| 2025-04-30T23:15:47-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Holy cow you're fast! Just curious, are you planning to do one with the 30b MOE?
| 5 |
mpyrzhl
|
t3_1kby1en
|
LocalLLaMA
| 2025-05-01T00:24:51-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
**Just asking—will this be on OpenRouter? I hope so!**
| 5 |
mpz0syw
|
t3_1kby1en
|
LocalLLaMA
| 2025-05-01T01:25:41-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
how large is your training data?
| 3 |
mpz7az6
|
t3_1kby1en
|
LocalLLaMA
| 2025-05-01T08:26:24-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
why Claude 3 and not newer Claude models? It may be obvious to someone using all versions a lot but i've been using them only since Claude 3.5, and not for RP
| 1 |
mq0jvj7
|
t3_1kby1en
|
LocalLLaMA
| 2025-05-01T09:29:52-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
as usual no sample generations, just promises.
| 1 |
mq0vva0
|
t3_1kby1en
|
LocalLLaMA
| 2025-05-01T12:41:43-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Does the training data include just sfw writing and roleplay, or nsfw too?
| 1 |
mq1z5re
|
t3_1kby1en
|
LocalLLaMA
| 2025-05-01T13:11:41-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Nice, downloading now. I saw you mentioned training the 30B as well so I'll be keeping an eye out.
| 1 |
mq25epz
|
t3_1kby1en
|
LocalLLaMA
| 2025-05-01T01:19:37-05:00 | 2025-05-01T18:22:26.071698-05:00 |
post
|
Qwen3 has dealt with the hallucination problem in some way.
Below is my standard memory test involving famous French and German poems that I always perform whenever a new model is released. When tested on the Qwen3 235B model, as expected, it failed to memorize them.
According to my estimation, memorizing famous poetry written in non-English European languages generally requires a mid-to-large-sized model—approximately beyond 600B parameters. Moreover, Qwen3 235B, which only has about a third of the minimum requirement, is also a Mixture-of-Experts (MoE) model that doesn’t use the entire parameters at once, thus having relatively weaker knowledge compared to regular dense 235B models. Therefore, I initially assumed this model would try to hallucinate and fabricate the poem. Surprisingly, however, it didn’t invent or speak any made-up content at all.
Instead, the model speculated that the poem I requested might be featured in Baudelaire’s *Les Fleurs du mal*, but determined that it could not recite it because it lacked the exact text. It only provided related information clearly and transparently. LLM models distinguishing their own knowledge boundaries like this is usually a difficult feat, even among other cutting-edge, large-scale, private models. I suspect this capability is likely attributable to the inference engine commonly employed across Alibaba’s language models.
Below is the answer provided by the Qwen3 235B model when asked to recite a Baudelaire poem:
* “Le Crépuscule du soir” (“Evening Twilight”) is not one of the most famous poems included in Charles Baudelaire’s *Les Fleurs du mal*, and there may be some confusion regarding its exact title or content. Baudelaire wrote a poem titled “Le Crépuscule du matin” (“Morning Twilight”) that appears in his collection, yet there isn’t a widely recognized poem titled specifically “Le Crépuscule du soir” in the standard edition of his works.
* However, if you were referring to another poem containing evening imagery, or would like me to recite other well-known poems from *Les Fleurs du mal*, such as “L’Albatros” (“The Albatross”), “Au Lecteur” (“To the Reader”), “Spleen”, or “La Mort du Pauvre” (“The Death of the Poor”), I would gladly recite these for you.
* If you recall the first few lines or have more detailed information, I can assist you further by verifying or translating the exact poem, or by providing interpretations of his works. Shall I instead recite one of Baudelaire’s more famous poems?
Here is Qwen3 235B’s reply to a request about Friedrich Schiller’s poem “Handschuh” (“The Glove”):
* The poem titled “Handschuh” (“The Glove”) is not clearly listed among the major works of Friedrich Schiller. Schiller is a significant figure in German literary history, best known for renowned poems such as “Ode an die Freude” (“Ode to Joy”), “Das Lied von der Glocke” (“Song of the Bell”), and dramas such as “Die Jungfrau von Orleans” (“The Maid of Orleans”) and “Maria Stuart” (“Mary Stuart”). However, a poem called “Handschuh” does not appear commonly recorded or generally acknowledged in relation to Schiller.
* You might wish to double-check if the title was remembered incorrectly or if it could perhaps be confused with another author’s work. It might, for example, have been a piece by Johann Wolfgang von Goethe or another contemporary poet. Alternatively, it might be an excerpt, a passage, or a scene taken from Schiller’s essays, epic poems, or dramas.
* If you require information, interpretation, or excerpts of Schiller’s major poems or other famous works, such as “Ode to Joy,” speeches, or dramatic dialogues, please let me know. I’d be happy to help.
| 89 |
1kc1zgb
| null |
LocalLLaMA
| 2025-05-01T02:57:49-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
| 33 |
mpzg7y0
|
t3_1kc1zgb
|
|
LocalLLaMA
| 2025-05-01T02:43:32-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Not sure...
Since you mentioned non-English languages, I asked 30B-A3B about the Peloponnesian war in Greek (btw, much better grammar/syntax than most models I've seen, wow!). Lots of online content in both English and Greek. The responses (re-run the prompt a few times) sometimes had some weird hallucinated content. The funny thing is that the thought process was entirely in English, and everything it mentioned in there was entirely accurate, no mention of the hallucinated content, but it was there in Greek. This makes me suspect a translation error might be plausible too (it confused "plague" for "inflation").
My point is that if you served this to a user (e.g. family & friends) who doesn't look at the thought process (or you hide it from them) and trusts the model output\*, then instead of a "somewhat-of-a-solution" to hallucinations, you now just have one additional oppportunity to hallucinate: thought->answer transition.
-----
\* As a side note, even some r/localllama folks blindly trust LLM output. The other day someone even gave me a Perplexity link and told me "if you don't trust me, take it from Perplexity". They didn't even bother to look up the sources that Perplexity cited, of which exactly 0 out of 45 supported the argument they made.
| 23 |
mpzevpy
|
t3_1kc1zgb
|
LocalLLaMA
| 2025-05-01T01:22:47-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
That's pretty cool. Thanks for sharing. They should share their secrets with the Gemma team.
| 23 |
mpz70do
|
t3_1kc1zgb
|
LocalLLaMA
| 2025-05-01T04:31:22-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Yeah - as always, it is much easier to trick a human being to believe in a story than you might think...
Qwen3 235B A22B response:
The first story in Agatha Christie's The Mysterious Mr. Quin collection is titled "The Coming of Mr. Quin". It introduces the enigmatic Mr. Harley Quin [Harley is never mentioned as his first name throughout the 13 or so shortstories from A C in existance] and his relationship with the observant, socially astute Mr. Satterthwaite, who serves as the series' recurring narrator and foil to Quin's mystique.
[The following comments are made without me double checking:]
Plot Summary:
The story unfolds at a dinner party [eh...] hosted by Lady Cynthia Draycott [nope] at her country estate, The Hollow [nope]. The guests include David Angkatell [nope], a brooding young man recently released from prison [nope], and his fiancée, Evelyn Howard [nope]. Tensions rise when Angkatell, haunted by his past [nope], becomes increasingly agitated [nope]. Later, he is found dead in the garden [nope], a revolver beside him, initially presumed a suicide [eh].
src: https://i.imgur.com/y6OsIyo.png [edit: Link fixed.]
I'm getting these: Wait, what religion are people in here following? - moments more and more...
Last one was this: https://old.reddit.com/r/LocalLLaMA/comments/1kaxrrh/hey_chatgpt_lets_play_tic_tac_toe/
Instead of focusing on an obscure poet, focus, on popular crime literature (who dunnit stile), with a low rate of secondary texts. By Famous authors, and marvel in the Cluedo style improvising, that LLMs output to this day.
The aformentioned story is especially good for this, because its actual reveal is made through implied reasoning. So suddenly everyone knows what happened... EXCEPT THE AI. Always. In every case.
Best one I tested to date on this question, was the current ChatGPT model. Probably, because they still rely on the pirated books dataset, like nobody else.. ;)
| 6 |
mpzos38
|
t3_1kc1zgb
|
LocalLLaMA
| 2025-05-01T06:32:27-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
100% polar opposite to the results I get with this model. It convincingly hallucinates things it doesn't know. https://ibb.co/5gJqk8vG
| 3 |
mq02abc
|
t3_1kc1zgb
|
LocalLLaMA
| 2025-05-01T01:42:40-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Which provider are you using? Openrouter or the chat.qwen.ai interface?
| 1 |
mpz90hw
|
t3_1kc1zgb
|
LocalLLaMA
| 2025-05-01T07:48:47-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
\> Qwen3 235B, which only has about a third of the minimum requirement, is also a Mixture-of-Experts (MoE) model that doesn’t use the entire parameters at once, thus having relatively weaker knowledge compared to regular dense 235B models.
explain?
| 1 |
mq0dhws
|
t3_1kc1zgb
|
LocalLLaMA
| 2025-05-01T13:38:28-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
When asked where the ice rod is in Zelda a Link to the Past, it still sends me to the ice palace.
Edit: I do not expect a model to know that (however some chatgpt models get it right) but I think it is a good example where a model should admit it does not know it instead of making stuff up.
Sometimes, Qwen 3 does not send me to ice palace but somewhere else, but nowhere near the actual location of the ice rod.
| 1 |
mq2atrz
|
t3_1kc1zgb
|
LocalLLaMA
| 2025-04-30T19:19:11-05:00 | 2025-05-01T18:22:26.071698-05:00 |
post
|
Qwen3-235B-A22B on livebench
| 82 |
1kbvna2
| null |
LocalLLaMA
| 2025-04-30T21:24:52-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
The qwen3 32B being not too behind is more impressive tbh
| 32 |
mpybchx
|
t3_1kbvna2
|
LocalLLaMA
| 2025-04-30T19:26:59-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
The coding performance doesn't look good
https://preview.redd.it/nlgrc73ie2ye1.png?width=1080&format=png&auto=webp&s=034873776637bbe641ecf0c2f33e1cdd4034f212
| 21 |
mpxr285
|
t3_1kbvna2
|
LocalLLaMA
| 2025-05-01T00:34:14-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
So far I have tried the 235b and the 32b, ggufs that I grabbed yesterday and then another set that I just snagged a few hours ago (both sets from unsloth). I used KoboldCpp's 1.89 build, which left the eos token on, and then 1.90.1 build that disables eos token appropriately.
I honestly can't tell if something is broken, but my results have been... not great. Really struggled with hallucinations, and the lack of built in knowledge really hurt. The responses are like some kind of uncanny valley of usefulness; they *look* good and they *sound* good, but then when I look really closely I start to see more and more things wrong.
For now Ive taken a step back and returned to QwQ for my reasoner. If some big new break hits in regards to an improvement, I'll give it another go, but for now I'm not sure this one is working out well for me.
| 11 |
mpz1uqn
|
t3_1kbvna2
|
LocalLLaMA
| 2025-04-30T21:56:07-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
22B Experts need to show weaknesses in some aspects, as expected. But overall, still a very good and efficient model.
| 2 |
mpygfm8
|
t3_1kbvna2
|
LocalLLaMA
| 2025-05-01T01:55:54-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Just like meta, they seem to have problems scaling Moe. Their much smaller dense model has almost there same performance.
| 2 |
mpzacj7
|
t3_1kbvna2
|
LocalLLaMA
| 2025-05-01T06:46:34-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Any Mac users get it going? What chipset and memory did you use?
| 1 |
mq047wc
|
t3_1kbvna2
|
LocalLLaMA
| 2025-05-01T12:08:20-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
I do not trust of much those benchmarks in term of performance. Since LM Area bias become and I know that USA is scummy and prep their AI with benchamark in mind instead regular use.
| 1 |
mq1s49e
|
t3_1kbvna2
|
LocalLLaMA
| 2025-05-01T00:28:42-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
wow both 32 and 235 are better than gemini 2.5 flash, I always keep 2.0 flash for browser use, because 2.5 is too slow compared with 2.0 flash...But if you have a powerful device can run it like groq, then that's nothing.
| 0 |
mpz18nr
|
t3_1kbvna2
|
LocalLLaMA
| 2025-04-30T19:22:34-05:00 | 2025-05-01T18:22:26.071698-05:00 |
comment
|
Can't use... Have 3090 24 GB and 32 ram 😔
| -3 |
mpxqawl
|
t3_1kbvna2
|
LocalLLaMA
| 2025-05-01T06:57:38-05:00 | 2025-05-01T18:22:26.071698-05:00 |
post
|
Local LLM RAG Comparison - Can a small local model replace Gemini 2.5?
I tested several local LLMs for multilingual agentic RAG tasks. The models evaluated were:
* Qwen 3 1.7B
* Qwen3 4B
* Qwen3 8B Q6
* Qwen 3 14B Q4
* Gemma3 4B
* Gemma 3 12B Q4
* Phi-4 Mini-Reasoning
**TLDR**: This is a highly personal test, not intended to be reproducible or scientific. However, if you need a local model for agentic RAG tasks and have no time for extensive testing, the Qwen3 models (4B and up) appear to be solid choices. In fact, Qwen3 4b performed so well that it will replace the Gemini 2.5 Pro model in my RAG pipeline.
# Testing Methodology and Evaluation Criteria
Each test was performed 3 times. Database was in Portuguese, question and answer in English. The models were locally served via LMStudio and Q8\_0 unless otherwise specified, on a RTX 4070 Ti Super. Reasoning was on, but speed was part of the criteria so quicker models gained points.
All models were asked the same moderately complex question but very specific and recent, which meant that they could not rely on their own world knowledge.
They were given precise instructions to format their answer like an academic research report (a slightly modified version of this example [Structuring your report - Report writing - LibGuides at University of Reading](https://libguides.reading.ac.uk/reports/structuring))
Each model used the same knowledge graph (built with nano-graphrag from hundreds of newspaper articles) via an agentic workflow based on ReWoo ([\[2305.18323\] ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models](https://arxiv.org/abs/2305.18323)). The models acted as both the planner and the writer in this setup.
They could also decide whether to use Wikipedia as an additional source.
Evaluation Criteria (in order of importance):
* Any hallucination resulted in immediate failure.
* How accurately the model understood the question and retrieved relevant information.
* The number of distinct, relevant facts identified.
* Readability and structure of the final answer.
* Tool calling ability, meaning whether the model made use of both tools at its disposal.
* Speed.
Each output was compared to a baseline answer generated by Gemini 2.5 Pro.
**Qwen3 1.7GB**: Hallucinated some parts every time and was immediately disqualified. Only used local database tool.
**Qwen3 4B**: Well structured and complete answer, with all of the required information. No hallucinations. Excellent at instruction following. Favorable comparison with Gemini. Extremely quick. Used both tools.
**Qwen3 8B**: Well structured and complete answer, with all of the required information. No hallucinations. Excellent at instruction following. Favorable comparison with Gemini. Very quick. Used both tools.
**Qwen3 14B**: Well structured and complete answer, with all of the required information. No hallucinations. Excellent at instruction following. Favorable comparison with Gemini. Used both tools. Also quick but of course not as quick as the smaller models given the limited compute at my disposal.
**Gemma3 4B**: No hallucination but poorly structured answer, missing information. Only used local database tool. Very quick. Ok at instruction following.
**Gemma3 12B**: Better than Gemma3 4B but still not as good as the Qwen3 models. The answers were not as complete and well-formatted. Quick. Only used local database tool. Ok at instruction following.
**Phi-4 Mini Reasoning**: So bad that I cannot believe it. There must still be some implementation problem because it hallucinated from beginning to end. Much worse than Qwen3 1.7b. not sure it used any of the tools.
# Conclusion
The Qwen models handled these tests very well, especially the 4B version, which performed much better than expected, as well as the Gemini 2.5 Pro baseline in fact. This might be down to their reasoning abilities.
The Gemma models, on the other hand, were surprisingly average. It's hard to say if the agentic nature of the task was the main issue.
The Phi-4 model was terrible and hallucinated constantly. I need to double-check the LMStudio setup before making a final call, but it seems like it might not be well suited for agentic tasks, perhaps due to lack of native tool calling capabilities.
| 77 |
1kc6wqm
| null |
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