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California’s A.B. 412: A Bill That Could Crush Startups and Cement A Big Tech AI Monopoly
| 109 | 2025-05-02T18:58:10 |
https://www.eff.org/deeplinks/2025/03/californias-ab-412-bill-could-crush-startups-and-cement-big-tech-ai-monopoly
|
fallingdowndizzyvr
|
eff.org
| 1970-01-01T00:00:00 | 0 |
{}
|
1kd8ugn
| false | null |
t3_1kd8ugn
|
/r/LocalLLaMA/comments/1kd8ugn/californias_ab_412_a_bill_that_could_crush/
| false | false | 109 |
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|
||
Train Better Computer-Use AI by Creating Human Demonstration Datasets
| 3 |
The C/ua team just released a new tutorial that shows how anyone with macOS can contribute to training better computer-use AI models by recording their own human demonstrations.
**Why this matters:**
One of the biggest challenges in developing AI that can use computers effectively is the lack of high-quality human demonstration data. Current computer-use models often fail to capture the nuanced ways humans navigate interfaces, recover from errors, and adapt to changing contexts.
This tutorial walks through using C/ua's Computer-Use Interface (CUI) with a Gradio UI to:
\- Record your natural computer interactions in a sandbox macOS environment
\- Organize and tag your demonstrations for maximum research value
\- Share your datasets on Hugging Face to advance computer-use AI research
What makes human demonstrations particularly valuable is that they capture aspects of computer use that synthetic data misses:
\- **Natural pacing** \- the rhythm of real human computer use
\- **Error recovery** \- how humans detect and fix mistakes
\- **Context-sensitive actions** \- adjusting behavior based on changing UI states
You can find the blog-post here: [https://trycua.com/blog/training-computer-use-models-trajectories-1](https://trycua.com/blog/training-computer-use-models-trajectories-1)
The only requirements are Python 3.10+ and macOS Sequoia.
Would love to hear if anyone else has been working on computer-use AI and your thoughts on this approach to building better training datasets!
| 2025-05-02T19:09:18 |
https://www.reddit.com/r/LocalLLaMA/comments/1kd949u/train_better_computeruse_ai_by_creating_human/
|
Original-Thanks-8118
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kd949u
| false | null |
t3_1kd949u
|
/r/LocalLLaMA/comments/1kd949u/train_better_computeruse_ai_by_creating_human/
| false | false |
self
| 3 | null |
Small models are funny
| 0 |
<think>
Okay, the user is asking about Cornwall's relationship with Wales over the centuries. Let me start by recalling what I know. Cornwall is a county in England, but there's also a region called Cornish in Wales. Wait, no, actually, Cornwall is part of England, but there's a Cornish language and culture in Cornwall, which is different from Wales. But the user might be confusing the two.
Wait, the user might be referring to Cornwall in Wales? No, Cornwall is in England. But there's a Cornish language in Cornwall, which is a Celtic language. However, there's also a Cornish language in Wales? No, that's not right. The Cornish language is spoken in Cornwall, England. But maybe the user is conflating Cornwall with the Welsh region. I need to clarify that.
[...]
This is using qwen3 4b Q3 K L.
| 2025-05-02T19:17:40 |
https://www.reddit.com/r/LocalLLaMA/comments/1kd9bfj/small_models_are_funny/
|
MrMrsPotts
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kd9bfj
| false | null |
t3_1kd9bfj
|
/r/LocalLLaMA/comments/1kd9bfj/small_models_are_funny/
| false | false |
self
| 0 | null |
Text Annotation with Chain-of-Thought
| 1 |
[removed]
| 2025-05-02T19:28:58 |
https://www.reddit.com/r/LocalLLaMA/comments/1kd9kuk/text_annotation_with_chainofthought/
|
Actual-Strawberry717
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kd9kuk
| false | null |
t3_1kd9kuk
|
/r/LocalLLaMA/comments/1kd9kuk/text_annotation_with_chainofthought/
| false | false |
self
| 1 | null |
Sam and OpenAI come clean with OpenAI issues last week - Sycophancy
| 0 |
Lot of people were complaining about 4o behaviour last week.
A deeper dive on their findings, what went wrong, and future changes they are making
While this is welcome change, end of the day it’s a reminder that’s closed models will have changes you won’t be aware of and vendors won’t come clean every time and there is no way this level of transparency is enough to run mission critical workloads
Long live open source models and long live LocalLLama!
| 2025-05-02T19:37:13 |
https://openai.com/index/expanding-on-sycophancy
|
Affectionate-Hat-536
|
openai.com
| 1970-01-01T00:00:00 | 0 |
{}
|
1kd9ruv
| false | null |
t3_1kd9ruv
|
/r/LocalLLaMA/comments/1kd9ruv/sam_and_openai_come_clean_with_openai_issues_last/
| false | false |
default
| 0 | null |
GPUStack parser detected as virus?
| 1 |
I just wanted to get feedback and thoughts on this just for peace of mind.
I installed GPUStack and it is fully functional. However, Norton detected one exe file, specifically GGUF Parser, to be a Trojan.
I ran it on virus total and it had all clears. Do you think Norton is just hitting a false positive because of its code structure?
I allowed it since it is actually pretty good to use and unlikely that it is malicious, but still I am always cautious.
Anyone else have this experience or thoughts on its parser dependency?
Thanks.
| 2025-05-02T19:37:43 |
https://www.reddit.com/r/LocalLLaMA/comments/1kd9sac/gpustack_parser_detected_as_virus/
|
randomoptionsdude
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kd9sac
| false | null |
t3_1kd9sac
|
/r/LocalLLaMA/comments/1kd9sac/gpustack_parser_detected_as_virus/
| false | false |
self
| 1 | null |
GPU/NPU accelerated inference on Android?
| 3 |
Does anyone know of an Android app that supports running local LLMs with GPU or NPU acceleration?
| 2025-05-02T19:44:49 |
https://www.reddit.com/r/LocalLLaMA/comments/1kd9y8v/gpunpu_accelerated_inference_on_android/
|
FluffyMoment2808
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kd9y8v
| false | null |
t3_1kd9y8v
|
/r/LocalLLaMA/comments/1kd9y8v/gpunpu_accelerated_inference_on_android/
| false | false |
self
| 3 | null |
Launching MCP Superassistant
| 2 |
[removed]
| 2025-05-02T19:46:38 |
https://www.reddit.com/r/LocalLLaMA/comments/1kd9zr4/launching_mcp_superassistant/
|
EfficientApartment52
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kd9zr4
| false | null |
t3_1kd9zr4
|
/r/LocalLLaMA/comments/1kd9zr4/launching_mcp_superassistant/
| false | false |
self
| 2 |
{'enabled': False, 'images': [{'id': 'tq_1IW12GDh8aCi_a1aEualhkr-P4sHQvyPWMtagNsQ', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/rjNWqZ9n6zaX2ncLPK2krdrWcSLzfa8KcRfAlXfAcsg.jpg?width=108&crop=smart&auto=webp&s=6d6602760af8d2d9ec98185329ec9140837bc149', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/rjNWqZ9n6zaX2ncLPK2krdrWcSLzfa8KcRfAlXfAcsg.jpg?width=216&crop=smart&auto=webp&s=21b9c17db5538b901a54607131f47a6b2936de75', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/rjNWqZ9n6zaX2ncLPK2krdrWcSLzfa8KcRfAlXfAcsg.jpg?width=320&crop=smart&auto=webp&s=fdd452852f8890c1bdc4691d38ae6e3daef35f39', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/rjNWqZ9n6zaX2ncLPK2krdrWcSLzfa8KcRfAlXfAcsg.jpg?width=640&crop=smart&auto=webp&s=6a0335e311a47f50e6d6bbb102ac19e27ea4559a', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/rjNWqZ9n6zaX2ncLPK2krdrWcSLzfa8KcRfAlXfAcsg.jpg?width=960&crop=smart&auto=webp&s=b313b4a9f911829b3bc740d47f3e71712e8dac80', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/rjNWqZ9n6zaX2ncLPK2krdrWcSLzfa8KcRfAlXfAcsg.jpg?width=1080&crop=smart&auto=webp&s=78d1c2ddcbc044c07027ece26d68b785282f4413', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/rjNWqZ9n6zaX2ncLPK2krdrWcSLzfa8KcRfAlXfAcsg.jpg?auto=webp&s=2c43d6d153707774e737722dbadd5833954e05ec', 'width': 1152}, 'variants': {}}]}
|
Easiest method for Local RAG on my book library?
| 10 |
I am not a coder or programmer. I have LM Studio up and running on Llama 3.1 8B. RTX 4090 + 128gb System RAM. Brand new and know very little.
I want to use Calibre to convert my owned books into plain text format (I presume) to run RAG on, indexing the contents so I can retrieve quotes rapidly, and ask abstract questions about the authors opinions and views, summarize chapters and ideas, etc.
What is the easiest way to do this? Haystack, Run pod (a free local version?), other?
As well, it seems the 8B model I am running is only 4-bit...? I am having trouble identifying how to get a better model on my system since I have 24gb VRAM and don't need super fast speed. I'd rather have more accuracy. It also seems 13B or something with more parameters is not available for Llama 3? Could I at least do 8B 16-bit (or even 8-bit) since I have the hardware?
| 2025-05-02T19:52:29 |
https://www.reddit.com/r/LocalLLaMA/comments/1kda4jw/easiest_method_for_local_rag_on_my_book_library/
|
filmguy123
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kda4jw
| false | null |
t3_1kda4jw
|
/r/LocalLLaMA/comments/1kda4jw/easiest_method_for_local_rag_on_my_book_library/
| false | false |
self
| 10 | null |
Best Hardware for Qwen3-30B-A3B CPU Inference?
| 4 |
Hey folks,
Like many here, I’ve been really impressed with 30B-A3B’s performance. Tested it on a few machines with different quants:
* 6-year-old laptop (i5-8250U, 32GB DDR4 @ 2400 MT/s): 7 t/s (q3\_k\_xl)
* i7-11 laptop (64GB DDR4): \~6-7 t/s (q4\_k\_xl)
* T14 Gen5 (DDR5): 15-20 t/s (q4\_k\_xl)
Solid results for usable outputs (RAG, etc.), so I’m thinking of diving deeper. Budget is $1k-2k (preferably on the lower end) for CPU inference (AM5 setup, prioritizing memory throughput over compute "power" - for the CPU... maybe a Ryzen 7 7700 (8C/16T) ?).
Thoughts? Is this the right path, or should I just grab an RTX 3090 instead? Or both? 😅
| 2025-05-02T19:57:00 |
https://www.reddit.com/r/LocalLLaMA/comments/1kda8bg/best_hardware_for_qwen330ba3b_cpu_inference/
|
ColdImplement1319
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kda8bg
| false | null |
t3_1kda8bg
|
/r/LocalLLaMA/comments/1kda8bg/best_hardware_for_qwen330ba3b_cpu_inference/
| false | false |
self
| 4 | null |
Bought 3090, need emotional support
| 1 |
[removed]
| 2025-05-02T20:05:19 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdafo8/bought_3090_need_emotional_support/
|
edeche
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdafo8
| false | null |
t3_1kdafo8
|
/r/LocalLLaMA/comments/1kdafo8/bought_3090_need_emotional_support/
| false | false |
self
| 1 | null |
What is RAG
| 0 |
How do i go about learning about it
| 2025-05-02T20:22:18 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdatw6/what_is_rag/
|
Both-Drama-8561
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdatw6
| false | null |
t3_1kdatw6
|
/r/LocalLLaMA/comments/1kdatw6/what_is_rag/
| false | false |
self
| 0 | null |
Best models for “rizz”
| 0 |
Wondering if anyone has suggestions on what models are currently the best at rizz as well as relevant prompts. I’m trying to setup an agent which can partially automate dating app interactions.
This is a serious post, not trolling.
| 2025-05-02T20:30:56 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdb1go/best_models_for_rizz/
|
GenericName-2
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdb1go
| false | null |
t3_1kdb1go
|
/r/LocalLLaMA/comments/1kdb1go/best_models_for_rizz/
| false | false |
self
| 0 | null |
Any idea why Qwen3 models are not showing in Aider or LMArena benchmarks?
| 16 |
Most of the other models used to be tested and listed in those benchmarks on the same day; however, I still can't find Qwen3 in either!
| 2025-05-02T20:38:28 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdb7t1/any_idea_why_qwen3_models_are_not_showing_in/
|
m_abdelfattah
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdb7t1
| false | null |
t3_1kdb7t1
|
/r/LocalLLaMA/comments/1kdb7t1/any_idea_why_qwen3_models_are_not_showing_in/
| false | false |
self
| 16 | null |
There is a big difference between use LM-Studio, Ollama, LLama.cpp?
| 43 |
Im mean for the use case of chat with the LLM. Not about others possible purpose.
Just that.
Im very new about this topic of LocalLLM. I ask my question to chatgpt and it says things that are not true, or at least are not true in the new version of LM-studio.
I try both LM-studio and Ollama.... i cant install Llama.cpp in my fedora 42...
About the two i try i dont notice nothing relevant, but of course, i do not make any test, etc.
So, for you that make test and have experience with this, JUST for chat about philosophy, there is a difference choosing between this?
thanks
| 2025-05-02T20:41:51 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdbamc/there_is_a_big_difference_between_use_lmstudio/
|
9acca9
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdbamc
| false | null |
t3_1kdbamc
|
/r/LocalLLaMA/comments/1kdbamc/there_is_a_big_difference_between_use_lmstudio/
| false | false |
self
| 43 | null |
Which LLM for coding in my little machine?
| 8 |
I have a 8vram and 32 ram.
What LLM just for code i can run?
Thanks
| 2025-05-02T20:45:41 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdbdok/which_llm_for_coding_in_my_little_machine/
|
9acca9
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdbdok
| false | null |
t3_1kdbdok
|
/r/LocalLLaMA/comments/1kdbdok/which_llm_for_coding_in_my_little_machine/
| false | false |
self
| 8 | null |
OK, MoE IS awesome
| 151 |
Recently I wrote posted this:
[https://www.reddit.com/r/LocalLLaMA/comments/1kc6cp7/moe\_is\_cool\_but\_does\_not\_solve\_speed\_when\_it/](https://www.reddit.com/r/LocalLLaMA/comments/1kc6cp7/moe_is_cool_but_does_not_solve_speed_when_it/)
I now want to correct myself as I have figured out that simply reducing a few layers (from 48 - 40) gives me **massive** more context!
I did not expect that as it seems that context VRAM / RAM consumption is not bound to total parameter count here but to the relatively tiny parameter count of the active experts! A normal 32B non-MoE model would require much more GB to achieve the same context length!
So with that setting I can safely have a context window of over 35k tokens with an initial speed of \~26 Tk/s instead of 109 Tk/s full speed.
(42154 context length = 22.8 GB VRAM idle, will grow when in use so I estimate 35K is safe) -> This is without flash attention or KV cache quantization, so even more should be possible with a single RTX 3090
That means with two RTX 3090 (only have one) I probably could use the full **131k context** window with nice speed with **qwen3-30b-a3b-128k**. (Q4\_K\_M)
So to conclude MoE solves the RAM consumption problem to a high degree, not fully but it improves the situation.
EDIT:
WITH flash attn and K and V cache quantization Q8 I get to over 100k context and 21.9 GB VRAM IDLE (will grow on usage, so IDK how much is really usable)
| 2025-05-02T21:04:06 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdbt84/ok_moe_is_awesome/
|
Ok-Scarcity-7875
|
self.LocalLLaMA
| 2025-05-02T21:17:13 | 0 |
{}
|
1kdbt84
| false | null |
t3_1kdbt84
|
/r/LocalLLaMA/comments/1kdbt84/ok_moe_is_awesome/
| false | false |
self
| 151 | null |
Fastest inference engine for Single Nvidia Card for a single user?
| 5 |
Absolute fastest engine to run models locally for an NVIDIA GPU and possibly a GUI to connect it to.
| 2025-05-02T21:04:20 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdbtfp/fastest_inference_engine_for_single_nvidia_card/
|
R46H4V
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdbtfp
| false | null |
t3_1kdbtfp
|
/r/LocalLLaMA/comments/1kdbtfp/fastest_inference_engine_for_single_nvidia_card/
| false | false |
self
| 5 | null |
Voice-to-Text Causing Thread Echo — Has Anyone Seen This?
| 1 |
[removed]
| 2025-05-02T21:10:52 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdbz09/voicetotext_causing_thread_echo_has_anyone_seen/
|
Oceanae
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdbz09
| false | null |
t3_1kdbz09
|
/r/LocalLLaMA/comments/1kdbz09/voicetotext_causing_thread_echo_has_anyone_seen/
| false | false |
self
| 1 | null |
RLHF WARNING: Excess politeness can trigger infinite praise loops.
| 35 | 2025-05-02T21:21:56 |
phoneixAdi
|
i.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdc83w
| false | null |
t3_1kdc83w
|
/r/LocalLLaMA/comments/1kdc83w/rlhf_warning_excess_politeness_can_trigger/
| false | false | 35 |
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|
|||
Meta AI latest work: LLM pretraining on consumer-graded GPU
| 48 |
Meta AI latest work: LLM pretraining on consumer-graded GPU
Title: GaLore 2: Large-Scale LLM Pre-Training by Gradient Low-Rank Projection
[https://www.arxiv.org/abs/2504.20437](https://www.arxiv.org/abs/2504.20437)
Large language models (LLMs) have revolutionized natural language understanding and generation but face significant memory bottlenecks during training. GaLore, Gradient Low-Rank Projection, addresses this issue by leveraging the inherent low-rank structure of weight gradients, enabling substantial memory savings without sacrificing performance. Recent works further extend GaLore from various aspects, including low-bit quantization and higher-order tensor structures. However, there are several remaining challenges for GaLore, such as the computational overhead of SVD for subspace updates and the integration with state-of-the-art training parallelization strategies (e.g., FSDP). In this paper, we present GaLore 2, an efficient and scalable GaLore framework that addresses these challenges and incorporates recent advancements. In addition, we demonstrate the scalability of GaLore 2 by pre-training Llama 7B from scratch using up to 500 billion training tokens, highlighting its potential impact on real LLM pre-training scenarios.
| 2025-05-02T21:28:37 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdcdjd/meta_ai_latest_work_llm_pretraining_on/
|
Dense-Smf-6032
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self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
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1kdcdjd
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/r/LocalLLaMA/comments/1kdcdjd/meta_ai_latest_work_llm_pretraining_on/
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self
| 48 | null |
GSM8k -flexible extract or strict match ?
| 1 |
[removed]
| 2025-05-02T21:28:59 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdcdtj/gsm8k_flexible_extract_or_strict_match/
|
StrawberryJunior3030
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdcdtj
| false | null |
t3_1kdcdtj
|
/r/LocalLLaMA/comments/1kdcdtj/gsm8k_flexible_extract_or_strict_match/
| false | false |
self
| 1 | null |
Which model has the best personality/vibes (open + closed)?
| 7 |
Hi guys, I just wanted to get your opinions on which model has the best personality/vibes?
For me:
GPT 4o is a beg and pick me
Gemini Pro and Flash just parrots back what you say to it
Qwen3 sometimes says the most unexpected things that are so silly it's funny after overthinking for ages
I know people hate on it, but llama 3.1 405b was so good and unhinged since it had so much Facebook data. The LLaMA 4 models are such a big let down since they're so restricted.
| 2025-05-02T21:36:29 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdck1b/which_model_has_the_best_personalityvibes_open/
|
z_3454_pfk
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdck1b
| false | null |
t3_1kdck1b
|
/r/LocalLLaMA/comments/1kdck1b/which_model_has_the_best_personalityvibes_open/
| false | false |
self
| 7 | null |
Foundation-Sec-8B Released (Cisco's Security-Focused Base Model)
| 37 |
Cisco's Foundation AI team just released Foundation-Sec-8B, a security-focused base model specifically designed for cybersecurity applications. It's a non-instruct, non-chat, non-reasoning model custom-tuned with security data. They announced follow up open-weight releases for the others.
This model, in the meantime, is designed to provide foundations for security tasks and vulnerability analysis.
Paper: https://arxiv.org/abs/2504.21039
| 2025-05-02T21:37:16 |
https://huggingface.co/fdtn-ai/Foundation-Sec-8B
|
Acceptable_Zombie136
|
huggingface.co
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdckor
| false | null |
t3_1kdckor
|
/r/LocalLLaMA/comments/1kdckor/foundationsec8b_released_ciscos_securityfocused/
| false | false | 37 |
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|
|
What graphics card should I buy? Which llama/qwent (etc.) model should I choose? Please help me, I'm a bit lost...
| 5 |
Well, I'm not a developer, far from it. I don't know anything about code, and I don't really intend to get into it.
I'm just a privacy-conscious user who would like to use a local AI model to:
- convert speech to text (hopefully understand medical language, or maybe learn it)
- format text and integrate it into Obsidian-like note-taking software
- monitor the literature for new scientific articles and summarize them
- be my personal assistant (for very important questions like: How do I get glue out of my daughter's hair? Draw me a unicorn to paint? Pain au chocolat or chocolatine?)
- if possible under Linux
So:
1 - Is it possible?
2 - With which model(s)? Llama? Gemma? Qwent?
3 - What graphics card should I get for this purpose? (Knowing that my budget is around 1000€)
| 2025-05-02T21:46:35 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdcs5a/what_graphics_card_should_i_buy_which_llamaqwent/
|
ed0c
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdcs5a
| false | null |
t3_1kdcs5a
|
/r/LocalLLaMA/comments/1kdcs5a/what_graphics_card_should_i_buy_which_llamaqwent/
| false | false |
self
| 5 | null |
Getting the same inference speed (13 t/s) with Qwen 30B-A3B on both CPU and GPU; Is this abnormal?
| 1 |
[removed]
| 2025-05-02T21:48:23 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdctk4/getting_the_same_inference_speed_13_ts_with_qwen/
|
yungfishstick
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdctk4
| false | null |
t3_1kdctk4
|
/r/LocalLLaMA/comments/1kdctk4/getting_the_same_inference_speed_13_ts_with_qwen/
| false | false |
self
| 1 | null |
Qwen3 32b Q8 on 3090 + 3060 + 3060
| 116 |
Building LocalLlama machine – Episode 2: Motherboard with 4 PCI-E slots
[In the previous episode](https://www.reddit.com/r/LocalLLaMA/comments/1kbnoyj/qwen3_on_2008_motherboard/) I was testing Qwen3 on motherboard from 2008, now I was able to put **3060+3060+3090** into **X399**.
I’ll likely need to use risers—both 3060s are touching, and one of them is running a bit **hot**. Eventually, I plan to add a second 3090, so better spacing will be necessary.
For the first time, I was able to run a **full 32B model in Q8** ***without*** **offloading to RAM**. I experimented with different configurations, assuming (quite reasonably!) that the 3090 is faster than the 3060. I’m seeing results between **11 and 15 tokens per second**.
How fast does Qwen3 32B run on *your* system?
As a bonus, I also tested the 14B model, so you can compare your results if you’re working with a smaller supercomputer. All 3 GPUs combined produced **28 t/s**, which is **slower than the 3090 alone at 49 t/s**. What’s the point of using 3060s if you can unleash the full power of a 3090?
I’ll be doing a lot more testing soon, but I wanted to share my initial results here.
I’ll probably try alternatives to `llama.cpp`, and I definitely need to test a large MoE model with this CPU.
| 2025-05-02T21:59:41 |
https://www.reddit.com/gallery/1kdd2zj
|
jacek2023
|
reddit.com
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t3_1kdd2zj
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/r/LocalLLaMA/comments/1kdd2zj/qwen3_32b_q8_on_3090_3060_3060/
| false | false | 116 | null |
|
Mixed precision KV cache quantization, Q8 for K / Q4 for V
| 4 |
Anyone tried this?
I found that Qwen3 0.6b comes with more KV heads which improves quality, but at \~4x larger VRAM usage.
Qwen2.5 0.5b coder: No. of Attention Heads (GQA): 14 for Q and 2 for KV.
Qwen3 0.6b: No. of Attention Heads (GQA): 16 for Q and 8 for KV.
With speculative decoding, this gets costly because llama.cpp does not quantize KV cache of the **draft** model. I lost 3GB out of 24GB because of this, which forced me to lower context length from 30K to 20K on my 24GB VRAM setup.
So now I'm considering more heavily quantizing KV cache for my Qwen3 32b **main** model: Q8 for K / Q4 for V instead of Q8 for both.
| 2025-05-02T22:11:00 |
https://www.reddit.com/r/LocalLLaMA/comments/1kddcdp/mixed_precision_kv_cache_quantization_q8_for_k_q4/
|
AdamDhahabi
|
self.LocalLLaMA
| 2025-05-02T22:38:03 | 0 |
{}
|
1kddcdp
| false | null |
t3_1kddcdp
|
/r/LocalLLaMA/comments/1kddcdp/mixed_precision_kv_cache_quantization_q8_for_k_q4/
| false | false |
self
| 4 | null |
What's the best model that can I use locally on this PC?
| 1 | 2025-05-02T22:25:10 |
TheMinarctics
|
i.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1kddntb
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t3_1kddntb
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| false | false | 1 |
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|
|||
Fugly little guy - v100 32gb 7945hx build
| 4 |
Funny build I did with my son. V100 32gb, we're going to do some basic inference models and ideally a lot of image and media generation. Thinking just pop_os/w11 dual boot.
No Flashpoint no problem!!
Any things I should try? This will be a pure hey kids let's mess around with x y z box.
If it works out well yes I will paint the fan shroud. I think it's charming!
| 2025-05-02T22:36:50 |
https://www.reddit.com/gallery/1kddx52
|
Only_Khlav_Khalash
|
reddit.com
| 1970-01-01T00:00:00 | 0 |
{}
|
1kddx52
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t3_1kddx52
|
/r/LocalLLaMA/comments/1kddx52/fugly_little_guy_v100_32gb_7945hx_build/
| false | false | 4 | null |
|
Trade off between knowledge and problem solving ability
| 19 |
I've noticed a trend where despite benchmark scores going up and companies claiming that their new small models are equivalent to older much bigger models, world knowledge of these new smaller models is worse than their larger predecessors, and often times worse than lower benchmarking models of similar sizes.
I have a set of private test questions that exercise coding, engineering problem solving, system threat modelling, and also ask specific knowledge questions on a variety of topics ranging from radio protocols and technical standards to local geography, history, and landmarks.
New models like Qwen 3 and GLM-4-0414 are vastly better at coding and problem solving than older models, but their knowledge is no better than older models and actually worse than some other similar sized older models. For example, Qwen 3 8B has considerably worse world knowledge in my tests than old models like Llama 3.1 8B and Gemma 2 9B. Likewise, Qwen 3 14B has much worse world knowledge than older weaker benchmarking models like Phi 4 and Gemma 3 12B. On a similar note, Granite 3.3 has slightly better coding/problem solving but slightly worse knowledge than Granite 3.2.
There are some exceptions to this trend though. Gemma 3 seems to have slightly better knowledge density than Gemma 2, while also having much better coding and problem solving. Gemma 3 is still very much a knowledge and writing model, and not particularly good at coding or problem solving, but much better at that than Gemma 2. Llama 4 Maverick has superb world knowledge, much better than Qwen 3 235B-A22, and actually slightly better than DeepSeek V3 in my tests, but its coding and problem solving abilities are mediocre. Llama 4 Maverick is under-appreciated for its knowledge; there's more to being smart than just being able to make balls bounce in a rotating heptagon or drawing a pelican on a bicycle. For knowledge based Q&A, it may be the best open/local model there is currently.
Anyway, what I'm getting at is that there seems to be a trade off between world knowledge and coding/problem solving ability for a given model size. Despite soaring benchmark scores, world knowledge of new models for a given size is stagnant or regressing. My guess is that this is because the training data for new models has more problem solving content and so proportionately less knowledge dense content. LLM makers have stopped publishing or highlighting scores for knowledge benchmarks like SimpleQA because those scores aren't improving and may be getting worse.
| 2025-05-02T22:53:09 |
https://www.reddit.com/r/LocalLLaMA/comments/1kde9mn/trade_off_between_knowledge_and_problem_solving/
|
Federal-Effective879
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kde9mn
| false | null |
t3_1kde9mn
|
/r/LocalLLaMA/comments/1kde9mn/trade_off_between_knowledge_and_problem_solving/
| false | false |
self
| 19 | null |
Looking for less VRAM hungry alternatives to vLLM for Qwen3 models
| 1 |
On the same GPU with 24 GB VRAM, I'm able to load the Qwen3 32B AWQ and run it without issues if I use hf transformers. With vLLM, I'm barely able to load Qwen3 14B AWQ because of how much VRAM it needs to use. Limiting `gpu_memory_utilization` doesn't really help because it'll just give me OOM errors. The problem is how naturally VRAM hungry vLLM is. I don't want to limit the context length of my model since I don't have to do it in transformers just to be able to load a model.
So what to do? I've tried SGLang, doesn't even start without nvcc (I have torch compiled, not sure why it keeps needing nvcc to compile torch again). I think there's ktransformers and llamacpp but not sure if they are any good with Qwen3 models. I want to be able to use AWQ models.
What do you use? What are your settings? Is there a way to make vLLM less hungry?
| 2025-05-02T23:04:16 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdeibm/looking_for_less_vram_hungry_alternatives_to_vllm/
|
No-Break-7922
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdeibm
| false | null |
t3_1kdeibm
|
/r/LocalLLaMA/comments/1kdeibm/looking_for_less_vram_hungry_alternatives_to_vllm/
| false | false |
self
| 1 | null |
Getting the same inference speed (13 t/s) with Qwen 30B-A3B on both CPU and GPU; Is this abnormal?
| 1 |
[removed]
| 2025-05-02T23:04:23 |
[deleted]
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdeier
| false | null |
t3_1kdeier
|
/r/LocalLLaMA/comments/1kdeier/getting_the_same_inference_speed_13_ts_with_qwen/
| false | false |
default
| 1 | null |
||
How useful are llm's as knowledge bases?
| 7 |
LLM's have lot's of knowledge but llm's can hallucinate. They also have a poor judgement of the accuracy of their own information. I have found that when it hallucinates, it often hallucinates things that are plausible or close to the truth but still wrong.
What is your experience of using llm's as a source of knowledge?
| 2025-05-02T23:16:27 |
https://www.reddit.com/r/LocalLLaMA/comments/1kderkz/how_useful_are_llms_as_knowledge_bases/
|
Tracing1701
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kderkz
| false | null |
t3_1kderkz
|
/r/LocalLLaMA/comments/1kderkz/how_useful_are_llms_as_knowledge_bases/
| false | false |
self
| 7 | null |
Built a persistent local AI with long-term memory and identity—need hardware help, not money, no strings.
| 1 |
[removed]
| 2025-05-02T23:46:30 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdfdwo/built_a_persistent_local_ai_with_longterm_memory/
|
Glad-Section9499
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdfdwo
| false | null |
t3_1kdfdwo
|
/r/LocalLLaMA/comments/1kdfdwo/built_a_persistent_local_ai_with_longterm_memory/
| false | false |
self
| 1 | null |
LLM progress nowadays is more about baking in more problems and knowledge than any groundbreaking innovations. For vast amount of problems, current models are in their final state.
| 18 |
What's your opinion about the above statement?
Am I alone in gut feelings that we've arrived?
| 2025-05-03T00:07:00 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdft52/llm_progress_nowadays_is_more_about_baking_in/
|
robertpiosik
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdft52
| false | null |
t3_1kdft52
|
/r/LocalLLaMA/comments/1kdft52/llm_progress_nowadays_is_more_about_baking_in/
| false | false |
self
| 18 | null |
Ollama: Qwen3-30b-a3b Faster on CPU over GPU
| 9 |
Is it possible that using CPU is better than GPU?
When I use just CPU (18 Core E5-2699 V3 128GB RAM**)** I get 19 response\_tokens/s.
But with GPU (Asus Phoenix RTX 3060 12GB VRAM) I only get 4 response\_tokens/s.
| 2025-05-03T00:29:06 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdg8iw/ollama_qwen330ba3b_faster_on_cpu_over_gpu/
|
benz1800
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdg8iw
| false | null |
t3_1kdg8iw
|
/r/LocalLLaMA/comments/1kdg8iw/ollama_qwen330ba3b_faster_on_cpu_over_gpu/
| false | false |
self
| 9 | null |
Chapter summaries using qwen3:30b-a3b
| 17 |
My sci-fi novel is about 85,000 words (500,000 characters) and split across 17 chapters. Due to its length, a shell script is used to summarize each chapter while including the summaries of all previous chapters for reference. In theory, this will shorten the input length (and processing time) significantly.
In each test, `ollama serve` is started with a particular context length, for example:
```
OLLAMA_CONTEXT_LENGTH=65535 ollama serve
```
The hardware is an NVIDIA T1000 8GB GPU and an AMD Ryzen 5 7600 6-Core Processor. Most tests used ollama 0.6.6. Now that ollama 0.6.7 is released, it's possible to try out llama4.
A script produces chapter summaries. At the end, the script uses xmlstarlet and xmllint to remove the `<think>` tag from the summary. Here are the results so far:
* **qwen3:30b-a3b** -- 32768 context. Several minor mistakes, overall quite accurate, stays true to the story, and takes hours to complete. Not much editing required.
* **llama3.3:70b-instruct-q4_K_M** -- 65535 context. Starts strong, eventually makes conceptual errors, loses its mind after chapter 14. Resetting gets it back on track, although still goes off the rails. I made numerous paragraph cuts to previous chapter summaries when re-running. Goes very slowly after 4 or 5 chapters, taking a long time to complete each chapter. I stopped at chapter 16 (of 17) because it was making things up. Lots of editing required.
* **phi4-reasoning** -- 32768 context. Gets many details wrong.
* **phi4-reasoning:plus** -- 32768 context. Gets details wrong.
* **deepseek-r1:32b** -- 32768 context. Makes stuff up.
llama4:scout is up next, possibly followed by a re-test of gemma3 and granite3, depending on the results.
Here are the file sizes for the summaries, so you can see they aren't blowing up in size:
$ wc -c summaries.qwen3/*txt | sed 's/summaries\.qwen3\///'
1202 01.txt
1683 02.txt
1664 03.txt
1860 04.txt
1816 05.txt
1859 06.txt
1726 07.txt
1512 08.txt
1574 09.txt
1394 10.txt
1552 11.txt
1476 12.txt
1568 13.txt
2093 14.txt
1230 15.txt
1747 16.txt
1391 17.txt
27347 total
The chapters themselves are larger (chapter 1 is the smallest, has a summary as the seed, and so is skipped):
$ wc -c ??.txt
20094 02.txt
25294 03.txt
23329 04.txt
20615 05.txt
26636 06.txt
26183 07.txt
27117 08.txt
34589 09.txt
34317 10.txt
31550 11.txt
22307 12.txt
28632 13.txt
40821 14.txt
45822 15.txt
41490 16.txt
43271 17.txt
Here's the script that runs ollama, including the prompt:
#!/usr/bin/env bash
OUTDIR=summaries
mkdir -p "${OUTDIR}"
readonly MODEL="llama4:scout"
BASE_PROMPT="You are a professional editor specializing in science fiction. Your task is to summarize a chapter faithfully without altering the user's ideas. The chapter text follows the 'CHAPTER TO SUMMARIZE:' marker below. Focus on key plot developments, character insights, and thematic elements. When ### appears in the text, it indicates separate scenes, so summarize each scene in its own paragraph, maintaining clear distinction between them. Write in clear, engaging language that captures the essence of each part. Provide the summary without introductory phrases. Text between 'PREVIOUS SUMMARIES FOR CONTEXT:' and 'CHAPTER TO SUMMARIZE:' is background information only, not content to summarize. Plain text and prosal form, a couple of paragraphs, 300 to 500 words."
for f in chapter/??.txt; do
prompt="${BASE_PROMPT}"
filename=$(basename "$f")
summaries="$(awk 'FNR==1 {print FILENAME ":"} 1' ${OUTDIR}/*.txt 2>/dev/null)"
outfile="${OUTDIR}/${filename}"
prompt+=$'\n\n'
if [ -n "${summaries}" ]; then
prompt+="PREVIOUS SUMMARIES FOR CONTEXT:"$'\n\n'$"${summaries}"$'\n\n'
fi
prompt+="--------------"$'\n\n'
prompt+="CHAPTER TO SUMMARIZE:"$'\n\n'"$(cat "$f")"$'\n\n'
echo "${prompt}" | ollama run ${MODEL} > "${outfile}"
echo "<root>$(cat ${outfile})</root>" | \
xmlstarlet ed -d '//think' | \
xmllint --xpath 'string(/)' - > "${OUTDIR}/result.txt"
mv -f "${OUTDIR}/result.txt" "${outfile}"
sleep 1
done
Here's the prompt with word wrapping:
> You are a professional editor specializing in science fiction. Your task is to summarize a chapter faithfully without altering the user's ideas. The chapter text follows the 'CHAPTER TO SUMMARIZE:' marker below. Focus on key plot developments, character insights, and thematic elements. When ### appears in the text, it indicates separate scenes, so summarize each scene in its own paragraph, maintaining clear distinction between them. Write in clear, engaging language that captures the essence of each part. Provide the summary without introductory phrases. Text between 'PREVIOUS SUMMARIES FOR CONTEXT:' and 'CHAPTER TO SUMMARIZE:' is background information only, not content to summarize. Plain text and prosal form, a couple of paragraphs, 300 to 500 words.
| 2025-05-03T00:36:57 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdge66/chapter_summaries_using_qwen330ba3b/
|
autonoma_2042
|
self.LocalLLaMA
| 2025-05-03T00:43:22 | 0 |
{}
|
1kdge66
| false | null |
t3_1kdge66
|
/r/LocalLLaMA/comments/1kdge66/chapter_summaries_using_qwen330ba3b/
| false | false |
self
| 17 | null |
Qwen 3 30B Pruned to 16B by Removing “Dead” Experts, 235B -> 150B Coming Soon!
| 1 |
[removed]
| 2025-05-03T01:12:08 |
[deleted]
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdh2pr
| false | null |
t3_1kdh2pr
|
/r/LocalLLaMA/comments/1kdh2pr/qwen_3_30b_pruned_to_16b_by_removing_dead_experts/
| false | false |
default
| 1 | null |
||
Qwen 3 30B Pruned to 16B by Removing “Dead” Experts, 235B -> 150B Coming Soon!
| 1 |
[removed]
| 2025-05-03T01:13:48 |
[deleted]
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdh3ub
| false | null |
t3_1kdh3ub
|
/r/LocalLLaMA/comments/1kdh3ub/qwen_3_30b_pruned_to_16b_by_removing_dead_experts/
| false | false |
default
| 1 | null |
||
Qwen 3 30B Pruned to 16B by Removing “Dead” Experts, 235B Pruned to 150B Coming Soon!
| 1 | 2025-05-03T01:14:40 |
https://huggingface.co/kalomaze/Qwen3-16B-A3B
|
TKGaming_11
|
huggingface.co
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdh4fn
| false | null |
t3_1kdh4fn
|
/r/LocalLLaMA/comments/1kdh4fn/qwen_3_30b_pruned_to_16b_by_removing_dead_experts/
| false | false | 1 |
{'enabled': False, 'images': [{'id': 'PLdAqga4Ibf2MXVtUkSz8cn7jAspOjB4qaMBTep7H-I', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/ySpO_7RYQ0SVsPSOMMWIe_bDz8SWExrfxn-1JYoQHZ8.jpg?width=108&crop=smart&auto=webp&s=a2e234728c54b4f662abcc84bb4a5477fab245cb', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/ySpO_7RYQ0SVsPSOMMWIe_bDz8SWExrfxn-1JYoQHZ8.jpg?width=216&crop=smart&auto=webp&s=9287125d47c29fe747a331a7ea8882238e7282f7', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/ySpO_7RYQ0SVsPSOMMWIe_bDz8SWExrfxn-1JYoQHZ8.jpg?width=320&crop=smart&auto=webp&s=db09a7f4ff9549f5a4dcedd3f49a48cb316f99be', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/ySpO_7RYQ0SVsPSOMMWIe_bDz8SWExrfxn-1JYoQHZ8.jpg?width=640&crop=smart&auto=webp&s=20bab6ab4e35ba7f6eda9be1e741fcf3e281b406', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/ySpO_7RYQ0SVsPSOMMWIe_bDz8SWExrfxn-1JYoQHZ8.jpg?width=960&crop=smart&auto=webp&s=1ab508af274dcb70500ffe63f64ebfcee168e42d', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/ySpO_7RYQ0SVsPSOMMWIe_bDz8SWExrfxn-1JYoQHZ8.jpg?width=1080&crop=smart&auto=webp&s=620207c371111512fe88408708e1835fc0bbe28c', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/ySpO_7RYQ0SVsPSOMMWIe_bDz8SWExrfxn-1JYoQHZ8.jpg?auto=webp&s=d3b5d7a23a7f5cbe63d723616da78538347ecef9', 'width': 1200}, 'variants': {}}]}
|
||
Qwen 3 30B Pruned to 16B by Leveraging Biased Router Distributions, 235B Pruned to 150B Coming Soon!
| 438 | 2025-05-03T01:18:05 |
https://huggingface.co/kalomaze/Qwen3-16B-A3B
|
TKGaming_11
|
huggingface.co
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdh6rl
| false | null |
t3_1kdh6rl
|
/r/LocalLLaMA/comments/1kdh6rl/qwen_3_30b_pruned_to_16b_by_leveraging_biased/
| false | false | 438 |
{'enabled': False, 'images': [{'id': 'PLdAqga4Ibf2MXVtUkSz8cn7jAspOjB4qaMBTep7H-I', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/ySpO_7RYQ0SVsPSOMMWIe_bDz8SWExrfxn-1JYoQHZ8.jpg?width=108&crop=smart&auto=webp&s=a2e234728c54b4f662abcc84bb4a5477fab245cb', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/ySpO_7RYQ0SVsPSOMMWIe_bDz8SWExrfxn-1JYoQHZ8.jpg?width=216&crop=smart&auto=webp&s=9287125d47c29fe747a331a7ea8882238e7282f7', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/ySpO_7RYQ0SVsPSOMMWIe_bDz8SWExrfxn-1JYoQHZ8.jpg?width=320&crop=smart&auto=webp&s=db09a7f4ff9549f5a4dcedd3f49a48cb316f99be', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/ySpO_7RYQ0SVsPSOMMWIe_bDz8SWExrfxn-1JYoQHZ8.jpg?width=640&crop=smart&auto=webp&s=20bab6ab4e35ba7f6eda9be1e741fcf3e281b406', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/ySpO_7RYQ0SVsPSOMMWIe_bDz8SWExrfxn-1JYoQHZ8.jpg?width=960&crop=smart&auto=webp&s=1ab508af274dcb70500ffe63f64ebfcee168e42d', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/ySpO_7RYQ0SVsPSOMMWIe_bDz8SWExrfxn-1JYoQHZ8.jpg?width=1080&crop=smart&auto=webp&s=620207c371111512fe88408708e1835fc0bbe28c', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/ySpO_7RYQ0SVsPSOMMWIe_bDz8SWExrfxn-1JYoQHZ8.jpg?auto=webp&s=d3b5d7a23a7f5cbe63d723616da78538347ecef9', 'width': 1200}, 'variants': {}}]}
|
||
Qwen/Qwen3-32B-AWQ just dropped for vLLM users!
| 1 |
[removed]
| 2025-05-03T01:48:01 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdhqjk/qwenqwen332bawq_just_dropped_for_vllm_users/
|
Training-Village1450
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdhqjk
| false | null |
t3_1kdhqjk
|
/r/LocalLLaMA/comments/1kdhqjk/qwenqwen332bawq_just_dropped_for_vllm_users/
| false | false |
self
| 1 | null |
Advice on Quant Size for GPU / CPU split for for Qwen3 235B-A22B (and in general?)
| 5 |
Hey locallamas!
I've been running models exclusively in VRAM to this point. My rubric for selecting a quant has always been: "What's the largest quant I can run that will fit within my VRAM given 32k context?"
Looking for advice on what quant size to try with [Qwen3 235B-A22B](https://huggingface.co/unsloth/Qwen3-235B-A22B-GGUF) knowing that I will need to load some of the model into RAM. I'd like to avoid downloading multiple 100-200 GB files.
[Unsloth Qwen3-235B-A22B Quants](https://preview.redd.it/19g42ijd7hye1.png?width=628&format=png&auto=webp&s=197657578ffaf3c7b08616766582d32a0529ac8c)
I have a reasonably powerful local rig: Single socket AMD EPYC 7402P with 512 GB of 2400 MT/s RAM and 6 RTX A4000s.
I assume my specific setup is relevant but that there is probably a rule of thumb or at least some intuition that you all can share.
I was thinking of going with one of the Q4s initially because that's typically the lowest I'm willing to go with GGUF. Then I stopped myself and thought I should ask some professionals.
| 2025-05-03T02:17:40 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdia1k/advice_on_quant_size_for_gpu_cpu_split_for_for/
|
x0xxin
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdia1k
| false | null |
t3_1kdia1k
|
/r/LocalLLaMA/comments/1kdia1k/advice_on_quant_size_for_gpu_cpu_split_for_for/
| false | false | 5 |
{'enabled': False, 'images': [{'id': 'rx3n0qdAkHK6iirdeW-jkcWJMyS3AefZQJIArfhCVr0', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/Ru6A7MPZmszNmMavBIJXZ-Qf6-aHWBhAoaKIh7tO0Ks.jpg?width=108&crop=smart&auto=webp&s=6c9699e848c6744d3541b58d5088430c8f383c39', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/Ru6A7MPZmszNmMavBIJXZ-Qf6-aHWBhAoaKIh7tO0Ks.jpg?width=216&crop=smart&auto=webp&s=a5c829e91fd83f00c99ca445eabd68bc7f4f53c9', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/Ru6A7MPZmszNmMavBIJXZ-Qf6-aHWBhAoaKIh7tO0Ks.jpg?width=320&crop=smart&auto=webp&s=26c54064d3772f765b4fc33cc0a988816e2d834b', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/Ru6A7MPZmszNmMavBIJXZ-Qf6-aHWBhAoaKIh7tO0Ks.jpg?width=640&crop=smart&auto=webp&s=7e5ca681836e6d44c749694b55962298027a4b34', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/Ru6A7MPZmszNmMavBIJXZ-Qf6-aHWBhAoaKIh7tO0Ks.jpg?width=960&crop=smart&auto=webp&s=5ac8b2ba7f716bb32e608e5ac9247afbd01f8314', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/Ru6A7MPZmszNmMavBIJXZ-Qf6-aHWBhAoaKIh7tO0Ks.jpg?width=1080&crop=smart&auto=webp&s=9bfceef4bfafaa2b50be907d375f3ff3ec1e9200', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/Ru6A7MPZmszNmMavBIJXZ-Qf6-aHWBhAoaKIh7tO0Ks.jpg?auto=webp&s=a4b0f149dbb55e174dcd3c9079bf234ac779a566', 'width': 1200}, 'variants': {}}]}
|
|
3x3060, 1x3090, 1x4080 SUPER
| 35 |
Qwen 32b q8 64k context - 20 tok/s
Llama 3.3 70b 16k context - 12 tok/s
Using Ollama because my board has too little RAM for vLLM. Upgrading the board this weekend:)
| 2025-05-03T02:22:10 |
https://www.reddit.com/gallery/1kdiczn
|
kevin_1994
|
reddit.com
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdiczn
| false | null |
t3_1kdiczn
|
/r/LocalLLaMA/comments/1kdiczn/3x3060_1x3090_1x4080_super/
| false | false | 35 | null |
|
whats the best ai for coding
| 1 |
[removed]
| 2025-05-03T02:27:23 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdigh6/whats_the_best_ai_for_coding/
|
Minute_Window_9258
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdigh6
| false | null |
t3_1kdigh6
|
/r/LocalLLaMA/comments/1kdigh6/whats_the_best_ai_for_coding/
| false | false |
self
| 1 | null |
Are instruct or text models better for coding?
| 12 |
Curious to hear what folks have found. There’s so many models to choose from, I’m not sure how to evaluate the general options when a new one becomes available
| 2025-05-03T02:28:08 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdigx8/are_instruct_or_text_models_better_for_coding/
|
SugarSafe1881
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdigx8
| false | null |
t3_1kdigx8
|
/r/LocalLLaMA/comments/1kdigx8/are_instruct_or_text_models_better_for_coding/
| false | false |
self
| 12 | null |
Getting the same inference speed (13 t/s) with Qwen 30B-A3B on both CPU and GPU; Is this abnormal?
| 1 |
[removed]
| 2025-05-03T02:42:39 |
[deleted]
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdiq94
| false | null |
t3_1kdiq94
|
/r/LocalLLaMA/comments/1kdiq94/getting_the_same_inference_speed_13_ts_with_qwen/
| false | false |
default
| 1 | null |
||
Voice recreation for Mother’s Day card
| 1 |
[removed]
| 2025-05-03T03:02:32 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdj36v/voice_recreation_for_mothers_day_card/
|
TraditionalLoan2951
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdj36v
| false | null |
t3_1kdj36v
|
/r/LocalLLaMA/comments/1kdj36v/voice_recreation_for_mothers_day_card/
| false | false |
self
| 1 | null |
GMKtek Evo-x2 LLM Performance
| 29 |
GMKTek claims Evo-X2 is 2.2 times faster than a 4090 in LM Studio. How so? Genuine question. I’m trying to learn more.
Other than total Ram, raw specs on the 5090 blow the Mini PC away…
| 2025-05-03T03:06:08 |
SimplestKen
|
i.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdj5gr
| false | null |
t3_1kdj5gr
|
/r/LocalLLaMA/comments/1kdj5gr/gmktek_evox2_llm_performance/
| false | false | 29 |
{'enabled': True, 'images': [{'id': 'P1vFZohI4Xhqt6W5AyNOizw6jPZ1KURLz17lW-XfbXA', 'resolutions': [{'height': 73, 'url': 'https://preview.redd.it/czx9oz7qghye1.jpeg?width=108&crop=smart&auto=webp&s=f834413fb716b12dc05d6c5152d34f52b67e0182', 'width': 108}, {'height': 147, 'url': 'https://preview.redd.it/czx9oz7qghye1.jpeg?width=216&crop=smart&auto=webp&s=d72f6f03ac04214b46f153436e442d8e298d0b28', 'width': 216}, {'height': 218, 'url': 'https://preview.redd.it/czx9oz7qghye1.jpeg?width=320&crop=smart&auto=webp&s=8ab664c323c4942b205a2c867429803e4151518a', 'width': 320}, {'height': 437, 'url': 'https://preview.redd.it/czx9oz7qghye1.jpeg?width=640&crop=smart&auto=webp&s=0f72072a2106fdc1c0a30f2f47a0fc1e43b238f0', 'width': 640}, {'height': 656, 'url': 'https://preview.redd.it/czx9oz7qghye1.jpeg?width=960&crop=smart&auto=webp&s=97d10a9acbcddd2bf6aa089565da48f91fac1bc7', 'width': 960}, {'height': 738, 'url': 'https://preview.redd.it/czx9oz7qghye1.jpeg?width=1080&crop=smart&auto=webp&s=57e7a27aac3cdbd0dbd1a2d21942dcd8968b5d1e', 'width': 1080}], 'source': {'height': 825, 'url': 'https://preview.redd.it/czx9oz7qghye1.jpeg?auto=webp&s=dad968509a0739eb86d418b48423569671f594f8', 'width': 1206}, 'variants': {}}]}
|
||
Good LM Studio Models for day to day tasks
| 1 |
[removed]
| 2025-05-03T05:26:49 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdlif2/good_lm_studio_models_for_day_to_day_tasks/
|
SharathSHebbar
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdlif2
| false | null |
t3_1kdlif2
|
/r/LocalLLaMA/comments/1kdlif2/good_lm_studio_models_for_day_to_day_tasks/
| false | false | 1 | null |
|
Qwen 3 32B + 8B have less censorship under RAG than other Qwen 3 models.
| 7 |
Did some testing last night with all the Qwen 3 models 32B and under and noticed something really interesting. Specifically, the 32B and 8B would comply with toxic requests in the presence of RAG. For example, it would give me methods to cook meth while the models of other sizes would refuse the request. If you do a cold request, all models will refuse. It seems like RAG is the answer if you really want to get the model to comply.
So far, the 8B model is a monster for its size in a RAG setup. It performs very well if it has information in the context you are looking for.
| 2025-05-03T05:40:15 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdlpwc/qwen_3_32b_8b_have_less_censorship_under_rag_than/
|
My_Unbiased_Opinion
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdlpwc
| false | null |
t3_1kdlpwc
|
/r/LocalLLaMA/comments/1kdlpwc/qwen_3_32b_8b_have_less_censorship_under_rag_than/
| false | false |
self
| 7 | null |
Need help in learning how you use local models for assistance/help in coding
| 1 |
[removed]
| 2025-05-03T05:40:29 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdlq14/need_help_in_learning_how_you_use_local_models/
|
_HarshMallow_
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdlq14
| false | null |
t3_1kdlq14
|
/r/LocalLLaMA/comments/1kdlq14/need_help_in_learning_how_you_use_local_models/
| false | false |
self
| 1 | null |
The little girl and her bodyguard
| 1 |
[removed]
| 2025-05-03T05:45:10 |
https://v.redd.it/j3y4rlpx8iye1
|
Ok-Maize-4629
|
v.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdlshj
| false |
{'reddit_video': {'bitrate_kbps': 2400, 'dash_url': 'https://v.redd.it/j3y4rlpx8iye1/DASHPlaylist.mpd?a=1748846310%2CZTMxMzkwMTE1ZGMxNWYwYTkwZDE4ZDg1MmZlOWYwMWViYzc5ZmZhZTI3MWIxZjJjZDg1MmQ4YTA5NGM0Yjk4Nw%3D%3D&v=1&f=sd', 'duration': 5, 'fallback_url': 'https://v.redd.it/j3y4rlpx8iye1/DASH_720.mp4?source=fallback', 'has_audio': True, 'height': 720, 'hls_url': 'https://v.redd.it/j3y4rlpx8iye1/HLSPlaylist.m3u8?a=1748846310%2CZDk3MDQ2Mjk1MzM3OGNiOThhMTYwM2ZhOWJmYzAzMTgzYTg1MzAyYjA2N2JmOWZmMmJjYmIyNmRmYjRhY2I0Ng%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/j3y4rlpx8iye1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 1280}}
|
t3_1kdlshj
|
/r/LocalLLaMA/comments/1kdlshj/the_little_girl_and_her_bodyguard/
| false | false | 1 |
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|
|
Plz help in understanding ai assisted coding
| 1 |
[removed]
| 2025-05-03T05:45:37 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdlsqk/plz_help_in_understanding_ai_assisted_coding/
|
_HarshMallow_
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdlsqk
| false | null |
t3_1kdlsqk
|
/r/LocalLLaMA/comments/1kdlsqk/plz_help_in_understanding_ai_assisted_coding/
| false | false |
self
| 1 | null |
Multimodal RAG with Cohere + Gemini 2.5 Flash
| 0 |
**Hi everyone! 👋**
I recently built a **Multimodal RAG (Retrieval-Augmented Generation)** system that can extract insights from **both text and images inside PDFs** — using **Cohere’s multimodal embeddings** and **Gemini 2.5 Flash**.
💡 **Why this matters:**
Traditional RAG systems completely miss visual data — like **pie charts, tables, or infographics** — that are critical in financial or research PDFs.
📽️ **Demo Video:**
https://reddit.com/link/1kdlwhp/video/07k4cb7y9iye1/player
📊 **Multimodal RAG in Action:**
✅ Upload a financial PDF
✅ Embed both text and images
✅ Ask any question — e.g., "How much % is Apple in S&P 500?"
✅ Gemini gives image-grounded answers like reading from a chart
https://preview.redd.it/d9mg38r4aiye1.png?width=1989&format=png&auto=webp&s=281f36c18a3780faf2fe62bda2e67db96603d88e
🧠 **Key Highlights:**
* Mixed FAISS index (text + image embeddings)
* Visual grounding via Gemini 2.5 Flash
* Handles questions from tables, charts, and even timelines
* Fully local setup using Streamlit + FAISS
🛠️ **Tech Stack:**
* **Cohere embed-v4.0** (text + image embeddings)
* **Gemini 2.5 Flash** (visual question answering)
* **FAISS** (for retrieval)
* **pdf2image** \+ **PIL** (image conversion)
* **Streamlit UI**
📌 **Full blog + source code + side-by-side demo:**
🔗 [sridhartech.hashnode.dev/beyond-text-building-multimodal-rag-systems-with-cohere-and-gemini](https://sridhartech.hashnode.dev/beyond-text-building-multimodal-rag-systems-with-cohere-and-gemini)
Would love to hear your thoughts or any feedback! 😊
| 2025-05-03T05:52:32 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdlwhp/multimodal_rag_with_cohere_gemini_25_flash/
|
srireddit2020
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdlwhp
| false | null |
t3_1kdlwhp
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/r/LocalLLaMA/comments/1kdlwhp/multimodal_rag_with_cohere_gemini_25_flash/
| false | false |
self
| 0 | null |
Are people here aware how good a deal AMD APUs are for LLMs, price/performance-wise?
| 0 |
I just found out that Ryzen APUs have something close to Apple’s unified memory. Sure, it's slower, maybe half the speed, but it costs WAY less. This exact mini PC (Ryzen 7735HS) is around $400 on Amazon. It runs Qwen3 30B A3B Q3 at \~25 tokens/sec.
So for $400 total, you get solid performance, no VRAM swapping hell like with discrete GPUs, and enough shared memory to load 20+GB models.
How many people here are even aware of this? Is something like this the future of inference? :D
| 2025-05-03T06:33:07 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdmi9m/are_people_here_aware_how_good_a_deal_amd_apus/
|
Sidran
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdmi9m
| false | null |
t3_1kdmi9m
|
/r/LocalLLaMA/comments/1kdmi9m/are_people_here_aware_how_good_a_deal_amd_apus/
| false | false |
self
| 0 | null |
Local search business API for Agents
| 1 |
[removed]
| 2025-05-03T06:36:41 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdmk37/local_search_business_api_for_agents/
|
EndComfortable2089
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdmk37
| false | null |
t3_1kdmk37
|
/r/LocalLLaMA/comments/1kdmk37/local_search_business_api_for_agents/
| false | false |
self
| 1 | null |
Tools on local models
| 1 |
[removed]
| 2025-05-03T06:56:23 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdmukv/tools_on_local_models/
|
Bradymodion
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdmukv
| false | null |
t3_1kdmukv
|
/r/LocalLLaMA/comments/1kdmukv/tools_on_local_models/
| false | false |
self
| 1 | null |
The little girl and her bodyguard youtub@
| 1 |
[removed]
| 2025-05-03T07:22:11 |
https://v.redd.it/mowx519ypiye1
|
Ok-Maize-4629
|
v.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdn8ev
| false |
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|
t3_1kdn8ev
|
/r/LocalLLaMA/comments/1kdn8ev/the_little_girl_and_her_bodyguard_youtub/
| false | false | 1 |
{'enabled': False, 'images': [{'id': 'b2k2Nmk2OXlwaXllMa1aiSopAvBha7bcPa64k2Wx2XOZhFR0KEIV9_BzknD3', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/b2k2Nmk2OXlwaXllMa1aiSopAvBha7bcPa64k2Wx2XOZhFR0KEIV9_BzknD3.png?width=108&crop=smart&format=pjpg&auto=webp&s=54c66eaa769c7e47a637c1a2418483489fb31100', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/b2k2Nmk2OXlwaXllMa1aiSopAvBha7bcPa64k2Wx2XOZhFR0KEIV9_BzknD3.png?width=216&crop=smart&format=pjpg&auto=webp&s=f0c4f6a8638218b5ef75ce7407d42f4391176e86', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/b2k2Nmk2OXlwaXllMa1aiSopAvBha7bcPa64k2Wx2XOZhFR0KEIV9_BzknD3.png?width=320&crop=smart&format=pjpg&auto=webp&s=a622b1e3bbc1e567cffa0a89fd5d4a3678456bf9', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/b2k2Nmk2OXlwaXllMa1aiSopAvBha7bcPa64k2Wx2XOZhFR0KEIV9_BzknD3.png?width=640&crop=smart&format=pjpg&auto=webp&s=52e53a3bfff67dfd8fff7d09e5f2e92e5dc4e20f', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/b2k2Nmk2OXlwaXllMa1aiSopAvBha7bcPa64k2Wx2XOZhFR0KEIV9_BzknD3.png?width=960&crop=smart&format=pjpg&auto=webp&s=c998c3fe8b3fcb95be62e99566564020b185b995', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/b2k2Nmk2OXlwaXllMa1aiSopAvBha7bcPa64k2Wx2XOZhFR0KEIV9_BzknD3.png?width=1080&crop=smart&format=pjpg&auto=webp&s=3de10022e019f8eeb5b22be87b6e5a3c368f68ac', 'width': 1080}], 'source': {'height': 720, 'url': 'https://external-preview.redd.it/b2k2Nmk2OXlwaXllMa1aiSopAvBha7bcPa64k2Wx2XOZhFR0KEIV9_BzknD3.png?format=pjpg&auto=webp&s=b4e644ec39b28b67844c49b1867133d12e795575', 'width': 1280}, 'variants': {}}]}
|
|
Help! Qwen 3 14B running too slow(1-2tk/s) on my laptop.
| 1 |
[removed]
| 2025-05-03T07:22:24 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdn8is/help_qwen_3_14b_running_too_slow12tks_on_my_laptop/
|
twistywackiness
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdn8is
| false | null |
t3_1kdn8is
|
/r/LocalLLaMA/comments/1kdn8is/help_qwen_3_14b_running_too_slow12tks_on_my_laptop/
| false | false |
self
| 1 | null |
How can i create a 24/7 coding agent?
| 1 |
[removed]
| 2025-05-03T07:22:38 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdn8n7/how_can_i_create_a_247_coding_agent/
|
gdox200
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdn8n7
| false | null |
t3_1kdn8n7
|
/r/LocalLLaMA/comments/1kdn8n7/how_can_i_create_a_247_coding_agent/
| false | false |
self
| 1 | null |
LLM with large context
| 0 |
What are some of your favorite LLMs to run locally with big context figures? Do we think its ever possible to hit 1M context locally in the next year or so?
| 2025-05-03T07:28:09 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdnbhj/llm_with_large_context/
|
CookieInstance
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdnbhj
| false | null |
t3_1kdnbhj
|
/r/LocalLLaMA/comments/1kdnbhj/llm_with_large_context/
| false | false |
self
| 0 | null |
32B Q4 or 14B Q8 for coding
| 1 |
[removed]
| 2025-05-03T07:29:25 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdnc2z/32b_q4_or_14b_q8_for_coding/
|
Over_Personality8171
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdnc2z
| false | null |
t3_1kdnc2z
|
/r/LocalLLaMA/comments/1kdnc2z/32b_q4_or_14b_q8_for_coding/
| false | false |
self
| 1 | null |
Terminal agentic coders is not so useful
| 1 |
There are a lot of IDE based agentic coders like cursor, windsurf, (vscode+roocode/cline), which gives better interface. What is the use of terminal coder like codex from openai, claude code from anthropic ?
| 2025-05-03T07:37:34 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdngak/terminal_agentic_coders_is_not_so_useful/
|
NovelNo2600
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdngak
| false | null |
t3_1kdngak
|
/r/LocalLLaMA/comments/1kdngak/terminal_agentic_coders_is_not_so_useful/
| false | false |
self
| 1 | null |
OpenAI charged on my credit card without my permission. I hate them.
| 0 |
I know it is not quite related to LocalLLaMA, but upset about it & want to tell a warning to who use OpenAI API.
I was using OpenAI API with prepaid balance. I never allowed automatic recharge, but they just charged unwanted amount $68 on my credit card without my consent.
My colleague used batch API without cost estimation. It was stopped in the middle due to low balance (which is ok). But, it resulted in -$68 (which is not ok). I was surprised - how it is possible?. I never agreed to pay beyond my prepaid amount. I assumed it's their fault, so I ignored the negative balance & forgot.
Two months later, today, they suddenly charged the minus balance on my credit card, without any notice or permission. I don't know how it is possible. I feel how bad they are.
This isn’t the first time OpenAI made me upset. I was using OpenAI API a lot until last year. They suddenly expired my balance to $0. Since then, I only put small amount like few tens. Sigh, topping small amount is not safe too, they charge on the saved credit card without permission.
Perhaps I will never pay OpenAI again. I don't expect them to be nice, but they shouldn't be bad as a business. I feel they are greedy.
Already, not using OpenAI at all. I tried DeepSeek API, costed $2 for the same job. Also, using local DeepSeek, and other good open models. Wish we get even better true-open models.
| 2025-05-03T07:52:13 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdnnms/openai_charged_on_my_credit_card_without_my/
|
smflx
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdnnms
| false | null |
t3_1kdnnms
|
/r/LocalLLaMA/comments/1kdnnms/openai_charged_on_my_credit_card_without_my/
| false | false |
self
| 0 | null |
LLM running on VR headset
| 1 |
[removed]
| 2025-05-03T08:17:35 |
Extension_Plastic669
|
i.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdo0jw
| false | null |
t3_1kdo0jw
|
/r/LocalLLaMA/comments/1kdo0jw/llm_running_on_vr_headset/
| false | false | 1 |
{'enabled': True, 'images': [{'id': 'C_ZnUAcwt7gYYZBAs_47STm1BNsP5ntYshNjCThtaW4', 'resolutions': [{'height': 81, 'url': 'https://preview.redd.it/q5fzh3x10jye1.jpeg?width=108&crop=smart&auto=webp&s=7fd99803516745e45a3a48504dfbb32ee59b28ae', 'width': 108}, {'height': 163, 'url': 'https://preview.redd.it/q5fzh3x10jye1.jpeg?width=216&crop=smart&auto=webp&s=b66823d6176a6720e3e89766eb2458c08bae95c9', 'width': 216}, {'height': 242, 'url': 'https://preview.redd.it/q5fzh3x10jye1.jpeg?width=320&crop=smart&auto=webp&s=75ae46d021bc636aac8cccc8899473b5f0fc57bc', 'width': 320}, {'height': 485, 'url': 'https://preview.redd.it/q5fzh3x10jye1.jpeg?width=640&crop=smart&auto=webp&s=9f2fa3647f15f485748268ebc2a7a461bd5e5e3f', 'width': 640}, {'height': 727, 'url': 'https://preview.redd.it/q5fzh3x10jye1.jpeg?width=960&crop=smart&auto=webp&s=554083f98ba4f7523314f5ca129b5bf67cf8d910', 'width': 960}, {'height': 818, 'url': 'https://preview.redd.it/q5fzh3x10jye1.jpeg?width=1080&crop=smart&auto=webp&s=7d24e4081b6b484d4dc615916484bfecf3928567', 'width': 1080}], 'source': {'height': 848, 'url': 'https://preview.redd.it/q5fzh3x10jye1.jpeg?auto=webp&s=59e343914427bcb8f974a5263184fa10bd634943', 'width': 1119}, 'variants': {}}]}
|
||
Hardware requirements for qwen3-30b-a3b? (At different quantizations)
| 6 |
Looking into a Local LLM for LLM related dev work (mostly RAG and MCP related). Anyone has any benchmarks for inference speed of qwen3-30b-a3b at Q4, Q8 and BF16 on different hardware?
Currently have a single Nvidia RTX 4090, but am open to buying more 3090s or 4090s to run this at good speeds.
| 2025-05-03T08:26:05 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdo4tf/hardware_requirements_for_qwen330ba3b_at/
|
AnEsportsFan
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdo4tf
| false | null |
t3_1kdo4tf
|
/r/LocalLLaMA/comments/1kdo4tf/hardware_requirements_for_qwen330ba3b_at/
| false | false |
self
| 6 | null |
Launching qomplement: the first OS native AI agent
| 1 |
[removed]
| 2025-05-03T08:33:43 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdo8sb/launching_qomplement_the_first_os_native_ai_agent/
|
kerimtaray
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdo8sb
| false | null |
t3_1kdo8sb
|
/r/LocalLLaMA/comments/1kdo8sb/launching_qomplement_the_first_os_native_ai_agent/
| false | false |
self
| 1 | null |
Launching qomplement: the first OS native AI agent
| 1 |
[removed]
| 2025-05-03T08:38:03 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdoazj/launching_qomplement_the_first_os_native_ai_agent/
|
Sad_Ad4916
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdoazj
| false | null |
t3_1kdoazj
|
/r/LocalLLaMA/comments/1kdoazj/launching_qomplement_the_first_os_native_ai_agent/
| false | false |
self
| 1 | null |
Qwen3 PAD token for IFT
| 1 |
[removed]
| 2025-05-03T08:39:49 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdobvg/qwen3_pad_token_for_ift/
|
AdInevitable3609
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdobvg
| false | null |
t3_1kdobvg
|
/r/LocalLLaMA/comments/1kdobvg/qwen3_pad_token_for_ift/
| false | false |
self
| 1 | null |
Launching qomplement: the first OS native AI agent
| 0 |
qomplement ships today. It’s a native agent that learns complete GUI workflows from demonstration data, so you can ask for something open-ended—“Plan a weekend trip to SF, grab the cheapest round-trip and some cool tours”—and it handles vision, long-horizon reasoning, memory and UI control in one shot. There’s no prompt-tuning grind and no brittle script chain; each execution refines the model, so it keeps working even when the interface changes.
Instead of relying on predefined rules or manual orchestration, qomplement is trained end-to-end on full interaction traces that pair what the user sees with what the agent does, letting it generalise across apps. That removes the maintenance overhead and fragility that plague classic RPA stacks and most current “agent frameworks.” One model books flights, edits slides, reconciles spreadsheets, then gets smarter after every run.
[qomplement.com](http://qomplement.com)
| 2025-05-03T08:40:15 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdoc32/launching_qomplement_the_first_os_native_ai_agent/
|
kerimtaray
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdoc32
| false | null |
t3_1kdoc32
|
/r/LocalLLaMA/comments/1kdoc32/launching_qomplement_the_first_os_native_ai_agent/
| false | false |
self
| 0 | null |
UI-Tars-1.5 reasoning never fails to entertain me.
| 1 | 2025-05-03T08:49:02 |
Successful_Bowl2564
|
i.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdogd2
| false | null |
t3_1kdogd2
|
/r/LocalLLaMA/comments/1kdogd2/uitars15_reasoning_never_fails_to_entertain_me/
| false | false | 1 |
{'enabled': True, 'images': [{'id': 'n0VJIBD7SMNgIPSjeUyHXUF8Hhz6aa8V8SIwlGAvAxo', 'resolutions': [{'height': 69, 'url': 'https://preview.redd.it/dzf9ejur5jye1.png?width=108&crop=smart&auto=webp&s=faebe52d0df13225122ad95866d982b51b0cd685', 'width': 108}, {'height': 138, 'url': 'https://preview.redd.it/dzf9ejur5jye1.png?width=216&crop=smart&auto=webp&s=e93f0aa115b913ab8446a6e3a596a02218af46bb', 'width': 216}, {'height': 204, 'url': 'https://preview.redd.it/dzf9ejur5jye1.png?width=320&crop=smart&auto=webp&s=af3fe1139375101f64e2c8e3ffa4d9fcde0ce0a6', 'width': 320}, {'height': 409, 'url': 'https://preview.redd.it/dzf9ejur5jye1.png?width=640&crop=smart&auto=webp&s=3817772ca8637cc531c15742f5053287092e6a82', 'width': 640}], 'source': {'height': 466, 'url': 'https://preview.redd.it/dzf9ejur5jye1.png?auto=webp&s=d5e71b92342544b8eb06c6884d95f45e24462bc8', 'width': 729}, 'variants': {}}]}
|
|||
Looking for a Fast and Emotionally Expressive Open-Source TTS Model
| 1 |
[removed]
| 2025-05-03T09:04:52 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdoob5/looking_for_a_fast_and_emotionally_expressive/
|
ConnectPea8944
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdoob5
| false | null |
t3_1kdoob5
|
/r/LocalLLaMA/comments/1kdoob5/looking_for_a_fast_and_emotionally_expressive/
| false | false |
self
| 1 | null |
How to offload all moe layers to cpu in oogabooga?
| 1 |
[removed]
| 2025-05-03T09:05:53 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdoou0/how_to_offload_all_moe_layers_to_cpu_in_oogabooga/
|
Slow_Comfort_2510
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdoou0
| false | null |
t3_1kdoou0
|
/r/LocalLLaMA/comments/1kdoou0/how_to_offload_all_moe_layers_to_cpu_in_oogabooga/
| false | false |
self
| 1 | null |
Looking for a Fast and Emotionally Expressive Open-Source TTS Model
| 1 |
[removed]
| 2025-05-03T09:06:49 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdopay/looking_for_a_fast_and_emotionally_expressive/
|
ConnectPea8944
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdopay
| false | null |
t3_1kdopay
|
/r/LocalLLaMA/comments/1kdopay/looking_for_a_fast_and_emotionally_expressive/
| false | false |
self
| 1 | null |
A new Study Claims LMArena Allows Big AI Labs to Game the Leaderboard
| 1 |
[removed]
| 2025-05-03T09:10:18 |
[deleted]
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdor01
| false | null |
t3_1kdor01
|
/r/LocalLLaMA/comments/1kdor01/a_new_study_claims_lmarena_allows_big_ai_labs_to/
| false | false |
default
| 1 | null |
||
New Study Claims Chatbot Arena Allows Big AI Labs to Game the Benchmark
| 1 |
[removed]
| 2025-05-03T09:14:20 |
[deleted]
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdosy1
| false | null |
t3_1kdosy1
|
/r/LocalLLaMA/comments/1kdosy1/new_study_claims_chatbot_arena_allows_big_ai_labs/
| false | false |
default
| 1 | null |
||
New Study Claims Leaderboard Allows the Big AI Labs to Game the Benchmark
| 1 |
[removed]
| 2025-05-03T09:15:07 |
[deleted]
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdotbg
| false | null |
t3_1kdotbg
|
/r/LocalLLaMA/comments/1kdotbg/new_study_claims_leaderboard_allows_the_big_ai/
| false | false |
default
| 1 | null |
||
New Study Claims Chatbot Arena Allows Big AI Labs to Game the Leaderboard!
| 1 |
[removed]
| 2025-05-03T09:17:35 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdoujw/new_study_claims_chatbot_arena_allows_big_ai_labs/
|
king_malebolgia_
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdoujw
| false | null |
t3_1kdoujw
|
/r/LocalLLaMA/comments/1kdoujw/new_study_claims_chatbot_arena_allows_big_ai_labs/
| false | false |
self
| 1 | null |
New Study Shows The Popular Benchmark Allows Big AI Labs to Game the Rankings!
| 1 |
[removed]
| 2025-05-03T09:20:56 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdow6i/new_study_shows_the_popular_benchmark_allows_big/
|
seytandiablo
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdow6i
| false | null |
t3_1kdow6i
|
/r/LocalLLaMA/comments/1kdow6i/new_study_shows_the_popular_benchmark_allows_big/
| false | false |
self
| 1 | null |
AI Fossil Record
| 1 | 2025-05-03T09:20:58 |
CommodoreCarbonate
|
i.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdow6y
| false | null |
t3_1kdow6y
|
/r/LocalLLaMA/comments/1kdow6y/ai_fossil_record/
| false | false | 1 |
{'enabled': True, 'images': [{'id': 'oIwNzf6TvULeVmp2TH1tBy23Q21NzNKWzh9Bv5cnNw0', 'resolutions': [{'height': 216, 'url': 'https://preview.redd.it/buvmkktjbjye1.png?width=108&crop=smart&auto=webp&s=74a724dd7d52e79d11cb5b22d10d7d04b6807e7f', 'width': 108}, {'height': 432, 'url': 'https://preview.redd.it/buvmkktjbjye1.png?width=216&crop=smart&auto=webp&s=5ed186faf638f07cc525151a5cf27adef62caee2', 'width': 216}, {'height': 640, 'url': 'https://preview.redd.it/buvmkktjbjye1.png?width=320&crop=smart&auto=webp&s=0a7daeb0c3a49be08aefb150b3f1110995fd9bd7', 'width': 320}], 'source': {'height': 1192, 'url': 'https://preview.redd.it/buvmkktjbjye1.png?auto=webp&s=a206facdb04d6dc9bb805481e684af7c64096373', 'width': 500}, 'variants': {}}]}
|
|||
Recommended models for focus on dialogue?
| 2 |
I'm looking for a model that focus on dialogue, and not so much on creating stories. It is going to be used to feed bots inside a WoW private server, so generating thoughts, meta-comments, etc... is not needed. If the training model used data or models that contain information about WoW, even better.
They know in which area they are, which class, level... and have their character cards generated that can be modified, so the models needs to also understand context and prompts properly.
| 2025-05-03T09:48:19 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdp9mp/recommended_models_for_focus_on_dialogue/
|
Chimpampin
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdp9mp
| false | null |
t3_1kdp9mp
|
/r/LocalLLaMA/comments/1kdp9mp/recommended_models_for_focus_on_dialogue/
| false | false |
self
| 2 | null |
Mistral-Small-3.1-24B-Instruct-2503 <32b UGI scores
| 86 |
It's been there for some time and I wonder why is nobody talking about it. I mean, from the handful of models that have a higher UGI score, all of them have lower natint and coding scores. Looks to me like an ideal choice for uncensored single-gpu inference? Plus, it supports tool usage. Am I missing something? :)
| 2025-05-03T10:08:00 |
Hujkis9
|
i.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdpjuz
| false | null |
t3_1kdpjuz
|
/r/LocalLLaMA/comments/1kdpjuz/mistralsmall3124binstruct2503_32b_ugi_scores/
| false | false | 86 |
{'enabled': True, 'images': [{'id': 'nC9RDlm7-UWHYo-E8-SegylazGfP3n9SGg-q9QxgMFQ', 'resolutions': [{'height': 72, 'url': 'https://preview.redd.it/brtajevzjjye1.jpeg?width=108&crop=smart&auto=webp&s=6ff4b54dcb6381a303185a634d4e32fd0b3101a4', 'width': 108}, {'height': 145, 'url': 'https://preview.redd.it/brtajevzjjye1.jpeg?width=216&crop=smart&auto=webp&s=40286fdc9fed6c39b3bb728d517cbd6b3b97980e', 'width': 216}, {'height': 215, 'url': 'https://preview.redd.it/brtajevzjjye1.jpeg?width=320&crop=smart&auto=webp&s=25fbf7185b36a8ae7a9910aff7c06f96c67d6a48', 'width': 320}, {'height': 431, 'url': 'https://preview.redd.it/brtajevzjjye1.jpeg?width=640&crop=smart&auto=webp&s=dc88e9798928b0e6c47dd1e936f8e2ecad4c4d28', 'width': 640}, {'height': 646, 'url': 'https://preview.redd.it/brtajevzjjye1.jpeg?width=960&crop=smart&auto=webp&s=52808984180cacc8ab21ff20dfdc028541b9b927', 'width': 960}, {'height': 727, 'url': 'https://preview.redd.it/brtajevzjjye1.jpeg?width=1080&crop=smart&auto=webp&s=1279287f8e01f3916ce542af7d1db05cdbecc0d2', 'width': 1080}], 'source': {'height': 1487, 'url': 'https://preview.redd.it/brtajevzjjye1.jpeg?auto=webp&s=39f24627781b09dd66b47e1f73a268539fe70eb1', 'width': 2208}, 'variants': {}}]}
|
||
aider polyglot - individual language results
| 8 |
the polyglot benchmarks give a combined result over different languages. is there published anywhere a breakdown of these by language. the reason is if i'm looking for a model to work on a particular language, i want to see which is the best for that specific language.
| 2025-05-03T10:10:53 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdplei/aider_polyglot_individual_language_results/
|
DeltaSqueezer
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdplei
| false | null |
t3_1kdplei
|
/r/LocalLLaMA/comments/1kdplei/aider_polyglot_individual_language_results/
| false | false |
self
| 8 | null |
I trained a Language Model to schedule events with GRPO! (full project inside)
| 71 |
I experimented with GRPO lately.
I am fascinated by models learning from prompts and rewards - no example answers needed like in Supervised Fine-Tuning.
After the DeepSeek boom, everyone is trying GRPO with GSM8K or the Countdown Game...
I wanted a different challenge, like ***teaching a model to create a schedule from a list of events and priorities***.
Choosing an original problem forced me to:
🤔 Think about the problem setting
🧬 Generate data
🤏 Choose the right base model
🏆 Design reward functions
🔄 Run multiple rounds of training, hoping that my model would learn something.
A fun and rewarding 😄 experience.
I learned a lot of things, that I want to share with you. 👇
✍️ Blog post: [https://huggingface.co/blog/anakin87/qwen-scheduler-grpo](https://huggingface.co/blog/anakin87/qwen-scheduler-grpo)
💻 Code: [https://github.com/anakin87/qwen-scheduler-grpo](https://github.com/anakin87/qwen-scheduler-grpo)
🤗 Hugging Face collection (dataset and model): [https://huggingface.co/collections/anakin87/qwen-scheduler-grpo-680bcc583e817390525a8837](https://huggingface.co/collections/anakin87/qwen-scheduler-grpo-680bcc583e817390525a8837)
🔥 Some hot takes from my experiment:
* GRPO is cool for verifiable tasks, but is more about eliciting desired behaviors from the trained model than teaching completely new stuff to it.
* Choosing the right base model (and size) matters.
* "Aha moment" might be over-hyped.
* Reward functions design is crucial. If your rewards are not robust, you might experience reward hacking (as it happened to me).
* Unsloth is great for saving GPU, but beware of bugs.
| 2025-05-03T10:34:41 |
anakin_87
|
i.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdpy20
| false | null |
t3_1kdpy20
|
/r/LocalLLaMA/comments/1kdpy20/i_trained_a_language_model_to_schedule_events/
| false | false | 71 |
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|
||
Curious about the JOSIEFIED versions of models on Ollama—are they safe?
| 1 |
[removed]
| 2025-05-03T10:39:11 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdq0k5/curious_about_the_josiefied_versions_of_models_on/
|
KrazyHomosapien
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdq0k5
| false | null |
t3_1kdq0k5
|
/r/LocalLLaMA/comments/1kdq0k5/curious_about_the_josiefied_versions_of_models_on/
| false | false |
self
| 1 |
{'enabled': False, 'images': [{'id': 'krjt_5uhqcaDfYjfO7lkezThehav9cAIRJgcK-OKAmM', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/s0D7i4Rco0trWh9Bu1uEkgnoJJLA3UNKUA9vs57seII.jpg?width=108&crop=smart&auto=webp&s=53486800d92d75b19d59502534fa9ba2785c14b0', 'width': 108}, {'height': 113, 'url': 'https://external-preview.redd.it/s0D7i4Rco0trWh9Bu1uEkgnoJJLA3UNKUA9vs57seII.jpg?width=216&crop=smart&auto=webp&s=b6f8fe68f176c90b3c2634702ce0e240165c319a', 'width': 216}, {'height': 168, 'url': 'https://external-preview.redd.it/s0D7i4Rco0trWh9Bu1uEkgnoJJLA3UNKUA9vs57seII.jpg?width=320&crop=smart&auto=webp&s=ba4a7df526b23a412363b0285eb9709218cd0a0b', 'width': 320}, {'height': 336, 'url': 'https://external-preview.redd.it/s0D7i4Rco0trWh9Bu1uEkgnoJJLA3UNKUA9vs57seII.jpg?width=640&crop=smart&auto=webp&s=1b231518e5ed41e809cceeaa1c12bf32733c2345', 'width': 640}, {'height': 504, 'url': 'https://external-preview.redd.it/s0D7i4Rco0trWh9Bu1uEkgnoJJLA3UNKUA9vs57seII.jpg?width=960&crop=smart&auto=webp&s=69bbae7110c0f929d0a3e6682fde693305633de7', 'width': 960}, {'height': 567, 'url': 'https://external-preview.redd.it/s0D7i4Rco0trWh9Bu1uEkgnoJJLA3UNKUA9vs57seII.jpg?width=1080&crop=smart&auto=webp&s=18433bdabee79410303b82563a6f388835945bef', 'width': 1080}], 'source': {'height': 630, 'url': 'https://external-preview.redd.it/s0D7i4Rco0trWh9Bu1uEkgnoJJLA3UNKUA9vs57seII.jpg?auto=webp&s=7a93b120137c378d21e25e2652789f870d1591a2', 'width': 1200}, 'variants': {}}]}
|
Why Llama3.2 asking somebody for help?
| 1 |
[removed]
| 2025-05-03T10:43:05 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdq2rh/why_llama32_asking_somebody_for_help/
|
Loud_Importance_8023
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdq2rh
| false | null |
t3_1kdq2rh
|
/r/LocalLLaMA/comments/1kdq2rh/why_llama32_asking_somebody_for_help/
| false | false |
self
| 1 | null |
Website for LLM Pricing
| 1 |
[removed]
| 2025-05-03T10:47:58 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdq5ex/website_for_llm_pricing/
|
Reception_Super
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdq5ex
| false | null |
t3_1kdq5ex
|
/r/LocalLLaMA/comments/1kdq5ex/website_for_llm_pricing/
| false | false |
self
| 1 |
{'enabled': False, 'images': [{'id': 'HUR4ZjSsMcPldBF8PlxclI3gg-mjZXBfe4bNavSwrFw', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/nhMEOcQ2pY2cWvmMC1a4Ya8l-ZpFkuu1hArRGS_70Jo.jpg?width=108&crop=smart&auto=webp&s=4c05659da71aabefa650df1fddb91bdf8888031d', 'width': 108}, {'height': 113, 'url': 'https://external-preview.redd.it/nhMEOcQ2pY2cWvmMC1a4Ya8l-ZpFkuu1hArRGS_70Jo.jpg?width=216&crop=smart&auto=webp&s=490f434fbbbf0f74a171e943297e61758633f730', 'width': 216}, {'height': 167, 'url': 'https://external-preview.redd.it/nhMEOcQ2pY2cWvmMC1a4Ya8l-ZpFkuu1hArRGS_70Jo.jpg?width=320&crop=smart&auto=webp&s=6f57ef706f7fd8fd0484669113c189fba8da9198', 'width': 320}, {'height': 335, 'url': 'https://external-preview.redd.it/nhMEOcQ2pY2cWvmMC1a4Ya8l-ZpFkuu1hArRGS_70Jo.jpg?width=640&crop=smart&auto=webp&s=5d72bc65c67e8fa81fbd23e548bba69e1a0bb3e8', 'width': 640}, {'height': 503, 'url': 'https://external-preview.redd.it/nhMEOcQ2pY2cWvmMC1a4Ya8l-ZpFkuu1hArRGS_70Jo.jpg?width=960&crop=smart&auto=webp&s=d13c9867058e25865b57356a8f76e4c2df202a84', 'width': 960}, {'height': 566, 'url': 'https://external-preview.redd.it/nhMEOcQ2pY2cWvmMC1a4Ya8l-ZpFkuu1hArRGS_70Jo.jpg?width=1080&crop=smart&auto=webp&s=84f7f12718fed77976904df46b50b7aeb1a2af03', 'width': 1080}], 'source': {'height': 629, 'url': 'https://external-preview.redd.it/nhMEOcQ2pY2cWvmMC1a4Ya8l-ZpFkuu1hArRGS_70Jo.jpg?auto=webp&s=0e18f26214a09b566dc3bc4bcdd70b1cf41d959a', 'width': 1200}, 'variants': {}}]}
|
What is the best local LLM that can produce NSFW stories? (including incest)
| 1 |
[removed]
| 2025-05-03T10:51:26 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdq79w/what_is_the_best_local_llm_that_can_produce_nsfw/
|
Majormanager2
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdq79w
| false | null |
t3_1kdq79w
|
/r/LocalLLaMA/comments/1kdq79w/what_is_the_best_local_llm_that_can_produce_nsfw/
| false | false |
nsfw
| 1 | null |
Teaching LLMs to use tools with RL! Successfully trained 0.5B/3B Qwen models to use a calculator tool 🔨
| 130 |
**👋 I recently had great fun training small language models (Qwen2.5 0.5B & 3B) to use a slightly complex calculator syntax through multi-turn reinforcement learning. Results were pretty cool: the 3B model went from 27% to 89% accuracy!**
**What I did:**
* Built a custom environment where model's output can be parsed & calculated
* Used Claude-3.5-Haiku as a reward model judge + software verifier
* Applied GRPO for training
* Total cost: \~$40 (\~£30) on rented GPUs
**Key results:**
* Qwen 0.5B: 0.6% → 34% accuracy (+33 points)
* Qwen 3B: 27% → 89% accuracy (+62 points)
**Technical details:**
* The model parses nested operations like: "What's the sum of 987 times 654, and 987 divided by the total of 321 and 11?"
* Uses XML/YAML format to structure calculator calls
* Rewards combine LLM judging + code verification
* 1 epoch training with 8 samples per prompt
My [Github repo](https://github.com/Danau5tin/calculator_agent_rl) has way more technical details if you're interested!
**Models are now on HuggingFace:**
* [Qwen 2.5 0.5B Calculator Agent](https://huggingface.co/Dan-AiTuning/calculator_agent_qwen2.5_0.5b)
* [Qwen 2.5 3B Calculator Agent](https://huggingface.co/Dan-AiTuning/calculator_agent_qwen2.5_3b)
Thought I'd share because I believe the future may tend toward multi-turn RL with tool use agentic LLMs at the center.
(Built using the [Verifiers](https://github.com/willccbb/verifiers) RL framework - It is a fantastic repo! Although not quite ready for prime time, it was extremely valuable)
| 2025-05-03T11:00:49 |
https://www.reddit.com/gallery/1kdqcjk
|
DanAiTuning
|
reddit.com
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdqcjk
| false | null |
t3_1kdqcjk
|
/r/LocalLLaMA/comments/1kdqcjk/teaching_llms_to_use_tools_with_rl_successfully/
| false | false | 130 |
{'enabled': True, 'images': [{'id': 'p1q0NLBPvDKer4wLl6MMJh8XzyT5jyGdKYucra-ZJAU', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/p1q0NLBPvDKer4wLl6MMJh8XzyT5jyGdKYucra-ZJAU.png?width=108&crop=smart&auto=webp&s=5dcfbc4addf367b21f6037c959c0992a64baf2a4', 'width': 108}, {'height': 113, 'url': 'https://external-preview.redd.it/p1q0NLBPvDKer4wLl6MMJh8XzyT5jyGdKYucra-ZJAU.png?width=216&crop=smart&auto=webp&s=8fe36212c2f5ceee0e40cd687b393b84c310ddde', 'width': 216}, {'height': 168, 'url': 'https://external-preview.redd.it/p1q0NLBPvDKer4wLl6MMJh8XzyT5jyGdKYucra-ZJAU.png?width=320&crop=smart&auto=webp&s=f66573791ec5020010dd47bd32e8976263d5961e', 'width': 320}, {'height': 336, 'url': 'https://external-preview.redd.it/p1q0NLBPvDKer4wLl6MMJh8XzyT5jyGdKYucra-ZJAU.png?width=640&crop=smart&auto=webp&s=f79f79565267c758b305dee548b6ca5439f9e39a', 'width': 640}, {'height': 504, 'url': 'https://external-preview.redd.it/p1q0NLBPvDKer4wLl6MMJh8XzyT5jyGdKYucra-ZJAU.png?width=960&crop=smart&auto=webp&s=269e3251eb6c6f3cefce551085e6aed8eeba5c29', 'width': 960}, {'height': 567, 'url': 'https://external-preview.redd.it/p1q0NLBPvDKer4wLl6MMJh8XzyT5jyGdKYucra-ZJAU.png?width=1080&crop=smart&auto=webp&s=8e1be6124219b4d743a700969b2f6e8ba4bdd53e', 'width': 1080}], 'source': {'height': 2656, 'url': 'https://external-preview.redd.it/p1q0NLBPvDKer4wLl6MMJh8XzyT5jyGdKYucra-ZJAU.png?auto=webp&s=e5dbe7d24cc207fe3419917f5896cf4a03072299', 'width': 5056}, 'variants': {}}]}
|
|
Are TTS models with voice cloning features inherently slower?
| 1 |
[removed]
| 2025-05-03T11:05:26 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdqfbs/are_tts_models_with_voice_cloning_features/
|
ConnectPea8944
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdqfbs
| false | null |
t3_1kdqfbs
|
/r/LocalLLaMA/comments/1kdqfbs/are_tts_models_with_voice_cloning_features/
| false | false |
self
| 1 | null |
What is the best local LLM that can produce NSFW stories? (including incest)
| 1 |
[removed]
| 2025-05-03T11:05:43 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdqfi1/what_is_the_best_local_llm_that_can_produce_nsfw/
|
Majormanager2
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdqfi1
| false | null |
t3_1kdqfi1
|
/r/LocalLLaMA/comments/1kdqfi1/what_is_the_best_local_llm_that_can_produce_nsfw/
| false | false |
nsfw
| 1 | null |
What are you guys currently using as a general use LLM?
| 1 |
[removed]
| 2025-05-03T11:06:05 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdqfox/what_are_you_guys_currently_using_as_a_general/
|
1234filip
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdqfox
| false | null |
t3_1kdqfox
|
/r/LocalLLaMA/comments/1kdqfox/what_are_you_guys_currently_using_as_a_general/
| false | false |
self
| 1 | null |
Qwen3 8b on android (it's not half bad)
| 105 |
A while ago, I decided to buy a phone with a Snapdragon 8 Gen 3 SoC.
Naturally, I wanted to push it beyond basic tasks and see how well it could handle local LLMs.
I set up [ChatterUI](https://github.com/Vali-98/ChatterUI), imported a model, and asked it a question. It took 101 seconds to respond— which is not bad at all, considering the model is typically designed for use on desktop GPUs.
---
And that brings me to the following question: what other models around this size (11B or lower) would you guys recommend?, did anybody else try this ?
The one I tested seems decent for general Q&A, but it's pretty bad at roleplay.
I'd really appreciate any suggestions for roleplay/translation/coding models that can work as efficiently.
Thank you!
| 2025-05-03T11:10:50 |
SofeyKujo
|
i.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdqibi
| false | null |
t3_1kdqibi
|
/r/LocalLLaMA/comments/1kdqibi/qwen3_8b_on_android_its_not_half_bad/
| false | false | 105 |
{'enabled': True, 'images': [{'id': 'UFodhob2hQDciMevuWed7YHSf7ljnXFNd4RO86MdovE', 'resolutions': [{'height': 203, 'url': 'https://preview.redd.it/54hu64e7vjye1.jpeg?width=108&crop=smart&auto=webp&s=29160579dbc1d625a490bd86a4f69354d471c9e1', 'width': 108}, {'height': 407, 'url': 'https://preview.redd.it/54hu64e7vjye1.jpeg?width=216&crop=smart&auto=webp&s=811bed4e01bd98f048522ff3df732d35a9e1d93d', 'width': 216}, {'height': 603, 'url': 'https://preview.redd.it/54hu64e7vjye1.jpeg?width=320&crop=smart&auto=webp&s=2beb6e054c2a439c2ba00d85c309c2b2fe0be421', 'width': 320}, {'height': 1207, 'url': 'https://preview.redd.it/54hu64e7vjye1.jpeg?width=640&crop=smart&auto=webp&s=0c65d6be61f794ea572b3b96b13c76ebaf908d18', 'width': 640}, {'height': 1811, 'url': 'https://preview.redd.it/54hu64e7vjye1.jpeg?width=960&crop=smart&auto=webp&s=14cd7d1d79a9067856950a3bff1852c5726ed0d6', 'width': 960}, {'height': 2038, 'url': 'https://preview.redd.it/54hu64e7vjye1.jpeg?width=1080&crop=smart&auto=webp&s=af9354ecabcd305337ab650e78f9b018f2e266cf', 'width': 1080}], 'source': {'height': 2378, 'url': 'https://preview.redd.it/54hu64e7vjye1.jpeg?auto=webp&s=2c414b32a89ddd93dc884874850cd22533fd733e', 'width': 1260}, 'variants': {}}]}
|
||
Qwen3-235B-A22B (no thinking) Seemingly Outperforms Claude 3.7 with 32k Thinking Tokens in Coding (Aider)
| 410 |
Came across this benchmark PR on Aider
I did my own benchmarks with aider and had consistent results
This is just impressive...
PR: [https://github.com/Aider-AI/aider/pull/3908/commits/015384218f9c87d68660079b70c30e0b59ffacf3](https://github.com/Aider-AI/aider/pull/3908/commits/015384218f9c87d68660079b70c30e0b59ffacf3)
Comment: [https://github.com/Aider-AI/aider/pull/3908#issuecomment-2841120815](https://github.com/Aider-AI/aider/pull/3908#issuecomment-2841120815)
| 2025-05-03T11:25:40 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdqqkp/qwen3235ba22b_no_thinking_seemingly_outperforms/
|
Greedy_Letterhead155
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdqqkp
| false | null |
t3_1kdqqkp
|
/r/LocalLLaMA/comments/1kdqqkp/qwen3235ba22b_no_thinking_seemingly_outperforms/
| false | false |
self
| 410 |
{'enabled': False, 'images': [{'id': 'GfbfVD7-Yoyq_ZANh5Du9YN8bpOMaQiy3IQRRns6u2E', 'resolutions': [{'height': 108, 'url': 'https://external-preview.redd.it/KJtSf9dWVwVmDA-yet3p3vtGffURoe5XXJYK41bF5p0.jpg?width=108&crop=smart&auto=webp&s=d64992e34734e9ee15594bbd76fbac785389f51f', 'width': 108}, {'height': 216, 'url': 'https://external-preview.redd.it/KJtSf9dWVwVmDA-yet3p3vtGffURoe5XXJYK41bF5p0.jpg?width=216&crop=smart&auto=webp&s=0d27d9129e019fd29b4d71723df77478b6151c81', 'width': 216}, {'height': 320, 'url': 'https://external-preview.redd.it/KJtSf9dWVwVmDA-yet3p3vtGffURoe5XXJYK41bF5p0.jpg?width=320&crop=smart&auto=webp&s=bbbaee4e79132434d5d3aa422c8712b82cad48fd', 'width': 320}], 'source': {'height': 400, 'url': 'https://external-preview.redd.it/KJtSf9dWVwVmDA-yet3p3vtGffURoe5XXJYK41bF5p0.jpg?auto=webp&s=4490eccd8004b6a92b9cef4f723cfdb077a6bd17', 'width': 400}, 'variants': {}}]}
|
What is the best LOCAL, OFFLINE and FREE AI generation tools right now?
| 0 |
For images and videos.
Thank you.
| 2025-05-03T11:46:24 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdr2uw/what_is_the_best_local_offline_and_free_ai/
|
Anto444_
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdr2uw
| false | null |
t3_1kdr2uw
|
/r/LocalLLaMA/comments/1kdr2uw/what_is_the_best_local_offline_and_free_ai/
| false | false |
self
| 0 | null |
360GB of VRAM. What model(s) would you serve and why?
| 1 |
FP8/Q8 quantization. Open discussion. What models do you choose? Context size? Use case? Number of people using it? What are you using to serve the model?
| 2025-05-03T11:47:22 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdr3eu/360gb_of_vram_what_models_would_you_serve_and_why/
|
ICanSeeYou7867
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdr3eu
| false | null |
t3_1kdr3eu
|
/r/LocalLLaMA/comments/1kdr3eu/360gb_of_vram_what_models_would_you_serve_and_why/
| false | false |
self
| 1 | null |
Keep dancing #beauti #lifeisbutadream #whowillbemylifepartnerta #fashion...
| 1 | 2025-05-03T11:56:43 |
https://youtube.com/watch?v=Dpv7lga8yNs&si=pLk8xUJ7NfEHhTr8
|
Ok-Maize-4629
|
youtube.com
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdr8zy
| false |
{'oembed': {'author_name': 'Ada', 'author_url': 'https://www.youtube.com/@Ada-y5f6o', 'height': 200, 'html': '<iframe width="356" height="200" src="https://www.youtube.com/embed/Dpv7lga8yNs?feature=oembed&enablejsapi=1" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen title="Keep dancing #beauti #lifeisbutadream #whowillbemylifepartnerta #fashion #dance"></iframe>', 'provider_name': 'YouTube', 'provider_url': 'https://www.youtube.com/', 'thumbnail_height': 360, 'thumbnail_url': 'https://i.ytimg.com/vi/Dpv7lga8yNs/hqdefault.jpg', 'thumbnail_width': 480, 'title': 'Keep dancing #beauti #lifeisbutadream #whowillbemylifepartnerta #fashion #dance', 'type': 'video', 'version': '1.0', 'width': 356}, 'type': 'youtube.com'}
|
t3_1kdr8zy
|
/r/LocalLLaMA/comments/1kdr8zy/keep_dancing_beauti_lifeisbutadream/
| false | false | 1 |
{'enabled': False, 'images': [{'id': 'r1Pig8lyWC3IOhg_Hph2FI3tPkw0tHqKGmf9iRpiH2U', 'resolutions': [{'height': 81, 'url': 'https://external-preview.redd.it/tEeUmoq4Eu6-SV7nq6XhuKMFvCqLrAqzleCR73cnRS0.jpg?width=108&crop=smart&auto=webp&s=1009c704171c81e0a112b452ab7f0977004e17fd', 'width': 108}, {'height': 162, 'url': 'https://external-preview.redd.it/tEeUmoq4Eu6-SV7nq6XhuKMFvCqLrAqzleCR73cnRS0.jpg?width=216&crop=smart&auto=webp&s=8d289840046f3bd66ff2d01207b3ee7adb5827d6', 'width': 216}, {'height': 240, 'url': 'https://external-preview.redd.it/tEeUmoq4Eu6-SV7nq6XhuKMFvCqLrAqzleCR73cnRS0.jpg?width=320&crop=smart&auto=webp&s=e1237f9808a8a8d1707b83763b3eb97a764b6c41', 'width': 320}], 'source': {'height': 360, 'url': 'https://external-preview.redd.it/tEeUmoq4Eu6-SV7nq6XhuKMFvCqLrAqzleCR73cnRS0.jpg?auto=webp&s=5ca061f37f4bc2bba665a415b7e522bbda1804f4', 'width': 480}, 'variants': {}}]}
|
||
Dia-JAX – Run a 1.6B Text-to-Speech Model on TPU with JAX
| 22 |
[JAX port of the Dia TTS model](https://github.com/jaco-bro/diajax) from Nari Labs for inference on any machine.
```
pip install diajax==0.0.7
dia --text "Hey, I'm really sorry for getting back to you so late. (cough) But voice cloning is just super easy, it's barely an inconvenience at all. I will show you how." --audio "assets/example_prompt.mp3"
```
| 2025-05-03T12:04:53 |
https://www.reddit.com/r/LocalLLaMA/comments/1kdre6g/diajax_run_a_16b_texttospeech_model_on_tpu_with/
|
Due-Yoghurt2093
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kdre6g
| false | null |
t3_1kdre6g
|
/r/LocalLLaMA/comments/1kdre6g/diajax_run_a_16b_texttospeech_model_on_tpu_with/
| false | false |
self
| 22 |
{'enabled': False, 'images': [{'id': 'GWi686FbGem5Ask2G6IvP3QnGq5qQIib9AMBZRELlmc', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/dnaQdJTlE8agIdU5SVo4tOWOeByImyWRL3CZXimcLHg.jpg?width=108&crop=smart&auto=webp&s=0a4486e57719afa6e40dd2078070e94af26c3330', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/dnaQdJTlE8agIdU5SVo4tOWOeByImyWRL3CZXimcLHg.jpg?width=216&crop=smart&auto=webp&s=24fa271d5f7c18e13b28312798a74209f109daf8', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/dnaQdJTlE8agIdU5SVo4tOWOeByImyWRL3CZXimcLHg.jpg?width=320&crop=smart&auto=webp&s=210a31d950a0ed3b81e6bf7bddd5258fd37b011b', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/dnaQdJTlE8agIdU5SVo4tOWOeByImyWRL3CZXimcLHg.jpg?width=640&crop=smart&auto=webp&s=5657a798278a960e06e24cce16d59a76c1cc3eec', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/dnaQdJTlE8agIdU5SVo4tOWOeByImyWRL3CZXimcLHg.jpg?width=960&crop=smart&auto=webp&s=2d43bbf2c3b3d7b687e972df1b3a960e5bb06cd1', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/dnaQdJTlE8agIdU5SVo4tOWOeByImyWRL3CZXimcLHg.jpg?width=1080&crop=smart&auto=webp&s=22482d82083f3704fef357fe0e1f18cbf0f498d0', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/dnaQdJTlE8agIdU5SVo4tOWOeByImyWRL3CZXimcLHg.jpg?auto=webp&s=575d979066605500bac14ae2711c6cfceb7ad1d5', 'width': 1200}, 'variants': {}}]}
|
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