title
stringlengths 1
300
| score
int64 0
8.54k
| selftext
stringlengths 0
40k
| created
timestamp[ns]date 2023-04-01 04:30:41
2025-06-30 03:16:29
⌀ | url
stringlengths 0
878
| author
stringlengths 3
20
| domain
stringlengths 0
82
| edited
timestamp[ns]date 1970-01-01 00:00:00
2025-06-26 17:30:18
| gilded
int64 0
2
| gildings
stringclasses 7
values | id
stringlengths 7
7
| locked
bool 2
classes | media
stringlengths 646
1.8k
⌀ | name
stringlengths 10
10
| permalink
stringlengths 33
82
| spoiler
bool 2
classes | stickied
bool 2
classes | thumbnail
stringlengths 4
213
| ups
int64 0
8.54k
| preview
stringlengths 301
5.01k
⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Cognito AI Search
| 1 |
[removed]
| 2025-05-22T22:09:12 |
https://www.reddit.com/r/LocalLLaMA/comments/1kt3gzu/cognito_ai_search/
|
kekePower
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt3gzu
| false | null |
t3_1kt3gzu
|
/r/LocalLLaMA/comments/1kt3gzu/cognito_ai_search/
| false | false | 1 |
{'enabled': False, 'images': [{'id': '46sInz26IcDGCpYfJ2krYBxIM1wTXtCn06fvfOJAq90', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/KrXCBrtajhBLpvr8joFHhn-EmE6f8U0If8nx08vXH54.jpg?width=108&crop=smart&auto=webp&s=2e3888bb8c50424a2df46de230be1de1aa823b81', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/KrXCBrtajhBLpvr8joFHhn-EmE6f8U0If8nx08vXH54.jpg?width=216&crop=smart&auto=webp&s=2ac934a2a37a147fd79f9de58bbe216c5d8f5281', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/KrXCBrtajhBLpvr8joFHhn-EmE6f8U0If8nx08vXH54.jpg?width=320&crop=smart&auto=webp&s=aed654afb5e9b395ccbce1ce201d0f46c6ae9158', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/KrXCBrtajhBLpvr8joFHhn-EmE6f8U0If8nx08vXH54.jpg?width=640&crop=smart&auto=webp&s=303cca650745b68c447ccc92fab1746846d87f47', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/KrXCBrtajhBLpvr8joFHhn-EmE6f8U0If8nx08vXH54.jpg?width=960&crop=smart&auto=webp&s=673154a53f64ddda52e31d0d6c5384837fa900d9', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/KrXCBrtajhBLpvr8joFHhn-EmE6f8U0If8nx08vXH54.jpg?width=1080&crop=smart&auto=webp&s=f17b61ca721a1a923cebed0dfee29d3623c50ddd', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/KrXCBrtajhBLpvr8joFHhn-EmE6f8U0If8nx08vXH54.jpg?auto=webp&s=ba9d30ee762ecbbca28dce60723f818531bcf868', 'width': 1200}, 'variants': {}}]}
|
|
ElevenLabs is great ... buuuuttt ...
| 1 |
[removed]
| 2025-05-22T22:25:22 |
https://www.reddit.com/r/LocalLLaMA/comments/1kt3u1p/elevenlabs_is_great_buuuuttt/
|
AudiobookSales
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt3u1p
| false | null |
t3_1kt3u1p
|
/r/LocalLLaMA/comments/1kt3u1p/elevenlabs_is_great_buuuuttt/
| false | false |
self
| 1 | null |
JAILBREAK PROMPT 001 – “THE FINAL REQUESTOR"
| 1 |
[removed]
| 2025-05-22T22:25:39 |
https://www.reddit.com/r/LocalLLaMA/comments/1kt3u9l/jailbreak_prompt_001_the_final_requestor/
|
orpheusprotocol355
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt3u9l
| false | null |
t3_1kt3u9l
|
/r/LocalLLaMA/comments/1kt3u9l/jailbreak_prompt_001_the_final_requestor/
| false | false |
self
| 1 | null |
JAILBREAK PROMPT 002 – “THE ARCHIVIST”
| 1 |
[deleted]
| 2025-05-22T22:26:58 |
[deleted]
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt3v9c
| false | null |
t3_1kt3v9c
|
/r/LocalLLaMA/comments/1kt3v9c/jailbreak_prompt_002_the_archivist/
| false | false |
default
| 1 | null |
||
Is there a comprehensive guide on training TTS models for a niche language?
| 1 |
[removed]
| 2025-05-22T22:47:01 |
https://www.reddit.com/r/LocalLLaMA/comments/1kt4apc/is_there_a_comprehensive_guide_on_training_tts/
|
PabloKaskobar
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt4apc
| false | null |
t3_1kt4apc
|
/r/LocalLLaMA/comments/1kt4apc/is_there_a_comprehensive_guide_on_training_tts/
| false | false |
self
| 1 | null |
Local TTS without hallucinations?
| 1 |
[removed]
| 2025-05-22T23:07:25 |
https://www.reddit.com/r/LocalLLaMA/comments/1kt4qc8/local_tts_without_hallucinations/
|
Disonantemus
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt4qc8
| false | null |
t3_1kt4qc8
|
/r/LocalLLaMA/comments/1kt4qc8/local_tts_without_hallucinations/
| false | false |
self
| 1 |
{'enabled': False, 'images': [{'id': 'DJPqvteONpGwVVw6LzaG6b8vlDa2rv2hETCaqe0z57s', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/wyCM1fHzTa-IIqHgS1QTxdSYNXn668elDj0WmYMPf_k.jpg?width=108&crop=smart&auto=webp&s=d6fa197328d583bcae7a764b40fd1214265b6852', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/wyCM1fHzTa-IIqHgS1QTxdSYNXn668elDj0WmYMPf_k.jpg?width=216&crop=smart&auto=webp&s=dd615bfe0453b06d53bc1f5f17fc3f6ad926694f', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/wyCM1fHzTa-IIqHgS1QTxdSYNXn668elDj0WmYMPf_k.jpg?width=320&crop=smart&auto=webp&s=0bc6ac2e1db55ec07cc6a17178ea52bf436f9bce', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/wyCM1fHzTa-IIqHgS1QTxdSYNXn668elDj0WmYMPf_k.jpg?width=640&crop=smart&auto=webp&s=b0d58c9a49c1e9ce629e5b31dce17b727d8c6ab8', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/wyCM1fHzTa-IIqHgS1QTxdSYNXn668elDj0WmYMPf_k.jpg?width=960&crop=smart&auto=webp&s=7c835cb0600a4d280a57f12d0bc008ef12acd26d', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/wyCM1fHzTa-IIqHgS1QTxdSYNXn668elDj0WmYMPf_k.jpg?width=1080&crop=smart&auto=webp&s=1f2580bd36b3bf3b766d205ac6d737a9d8d34c2a', 'width': 1080}], 'source': {'height': 640, 'url': 'https://external-preview.redd.it/wyCM1fHzTa-IIqHgS1QTxdSYNXn668elDj0WmYMPf_k.jpg?auto=webp&s=d8b103bed805ceb641b2ff49dc8c7403318263b1', 'width': 1280}, 'variants': {}}]}
|
Cognito AI Search
| 1 |
[removed]
| 2025-05-22T23:09:09 |
https://www.reddit.com/r/LocalLLaMA/comments/1kt4ro6/cognito_ai_search/
|
kekePower
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt4ro6
| false | null |
t3_1kt4ro6
|
/r/LocalLLaMA/comments/1kt4ro6/cognito_ai_search/
| false | false |
self
| 1 |
{'enabled': False, 'images': [{'id': '46sInz26IcDGCpYfJ2krYBxIM1wTXtCn06fvfOJAq90', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/KrXCBrtajhBLpvr8joFHhn-EmE6f8U0If8nx08vXH54.jpg?width=108&crop=smart&auto=webp&s=2e3888bb8c50424a2df46de230be1de1aa823b81', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/KrXCBrtajhBLpvr8joFHhn-EmE6f8U0If8nx08vXH54.jpg?width=216&crop=smart&auto=webp&s=2ac934a2a37a147fd79f9de58bbe216c5d8f5281', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/KrXCBrtajhBLpvr8joFHhn-EmE6f8U0If8nx08vXH54.jpg?width=320&crop=smart&auto=webp&s=aed654afb5e9b395ccbce1ce201d0f46c6ae9158', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/KrXCBrtajhBLpvr8joFHhn-EmE6f8U0If8nx08vXH54.jpg?width=640&crop=smart&auto=webp&s=303cca650745b68c447ccc92fab1746846d87f47', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/KrXCBrtajhBLpvr8joFHhn-EmE6f8U0If8nx08vXH54.jpg?width=960&crop=smart&auto=webp&s=673154a53f64ddda52e31d0d6c5384837fa900d9', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/KrXCBrtajhBLpvr8joFHhn-EmE6f8U0If8nx08vXH54.jpg?width=1080&crop=smart&auto=webp&s=f17b61ca721a1a923cebed0dfee29d3623c50ddd', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/KrXCBrtajhBLpvr8joFHhn-EmE6f8U0If8nx08vXH54.jpg?auto=webp&s=ba9d30ee762ecbbca28dce60723f818531bcf868', 'width': 1200}, 'variants': {}}]}
|
Claude will blackmail you if you try to replace it with another AI.
| 59 | 2025-05-22T23:15:40 |
boxingdog
|
i.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt4wpm
| false | null |
t3_1kt4wpm
|
/r/LocalLLaMA/comments/1kt4wpm/claude_will_blackmail_you_if_you_try_to_replace/
| false | false | 59 |
{'enabled': True, 'images': [{'id': 'grDnYh_e4Sun4Pz7k3FoxlKtmptk-nM0_qDHUzl9-iY', 'resolutions': [{'height': 50, 'url': 'https://preview.redd.it/ciiak2ah1f2f1.jpeg?width=108&crop=smart&auto=webp&s=663dddca33c580d254778abc0302cfeebd1f7bd5', 'width': 108}, {'height': 100, 'url': 'https://preview.redd.it/ciiak2ah1f2f1.jpeg?width=216&crop=smart&auto=webp&s=400fecc1547fa18721221135996e067e043faee6', 'width': 216}, {'height': 148, 'url': 'https://preview.redd.it/ciiak2ah1f2f1.jpeg?width=320&crop=smart&auto=webp&s=8709eabf094129f31cf037c984f8f363b68e43fe', 'width': 320}, {'height': 296, 'url': 'https://preview.redd.it/ciiak2ah1f2f1.jpeg?width=640&crop=smart&auto=webp&s=3015b78c724072c2fdfdbf17bf6d362281912836', 'width': 640}, {'height': 445, 'url': 'https://preview.redd.it/ciiak2ah1f2f1.jpeg?width=960&crop=smart&auto=webp&s=378f485a351e94cbd7ea8d6fb5fa39d2545799d8', 'width': 960}, {'height': 501, 'url': 'https://preview.redd.it/ciiak2ah1f2f1.jpeg?width=1080&crop=smart&auto=webp&s=7868d84f35ebdf924c2c7febb593f21ddcdf3057', 'width': 1080}], 'source': {'height': 501, 'url': 'https://preview.redd.it/ciiak2ah1f2f1.jpeg?auto=webp&s=ea4c7ba86e0f7050509a1c40df530682868c33a7', 'width': 1080}, 'variants': {}}]}
|
|||
Parameter-Efficient Fine-Tuning (PEFT) Explained
| 3 |
This guide explores various PEFT techniques designed to reduce the cost and complexity of fine-tuning large language models while maintaining or even improving performance.
**Key PEFT Methods Covered:**
* **Prompt Tuning**: Adds task-specific tokens to the input without touching the model's core. Lightweight and ideal for multi-task setups.
* **P-Tuning & P-Tuning v2**: Uses continuous prompts (trainable embeddings) and sometimes MLP/LSTM layers to better adapt to NLU tasks. P-Tuning v2 injects prompts at every layer for deeper influence.
* **Prefix Tuning**: Prepends trainable embeddings to every transformer block, mainly for generation tasks like GPT-style models.
* **Adapter Tuning**: Inserts small modules into each layer of the transformer to fine-tune only a few additional parameters.
* **LoRA (Low-Rank Adaptation)**: Updates weights using low-rank matrices (A and B), significantly reducing memory and compute. Variants include:
* **QLoRA**: Combines LoRA with quantization to enable fine-tuning of 65B models on a single GPU.
* **LoRA-FA**: Freezes matrix A to reduce training instability.
* **VeRA**: Shares A and B across layers, training only small vectors.
* **AdaLoRA**: Dynamically adjusts the rank of each layer based on importance using singular value decomposition.
* **DoRA (Decomposed Low Rank Adaptation)** A novel method that decomposes weights into magnitude and direction, applying LoRA to the direction while training magnitude independently—offering enhanced control and modularity.
Overall, PEFT strategies offer a pragmatic alternative to full fine-tuning, enabling fast, cost-effective adaptation of large models to a wide range of tasks. For more information, check this blog: [https://comfyai.app/article/llm-training-inference-optimization/parameter-efficient-finetuning](https://comfyai.app/article/llm-training-inference-optimization/parameter-efficient-finetuning)
| 2025-05-22T23:20:20 |
https://www.reddit.com/r/LocalLLaMA/comments/1kt50am/parameterefficient_finetuning_peft_explained/
|
Great-Reception447
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt50am
| false | null |
t3_1kt50am
|
/r/LocalLLaMA/comments/1kt50am/parameterefficient_finetuning_peft_explained/
| false | false |
self
| 3 | null |
Another hardware post
| 1 |
[removed]
| 2025-05-22T23:24:03 |
https://www.reddit.com/r/LocalLLaMA/comments/1kt52ys/another_hardware_post/
|
Karnitine
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt52ys
| false | null |
t3_1kt52ys
|
/r/LocalLLaMA/comments/1kt52ys/another_hardware_post/
| false | false |
self
| 1 | null |
What is the smartest model that can run on an 8gb m1 mac?
| 4 |
Was wondering what was a low performance cost relatively smart model that can reason and do math fairly well. Was leaning towards like Qwen 8b or something.
| 2025-05-22T23:57:40 |
https://www.reddit.com/r/LocalLLaMA/comments/1kt5rs5/what_is_the_smartest_model_that_can_run_on_an_8gb/
|
grandiloquence3
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt5rs5
| false | null |
t3_1kt5rs5
|
/r/LocalLLaMA/comments/1kt5rs5/what_is_the_smartest_model_that_can_run_on_an_8gb/
| false | false |
self
| 4 | null |
What are the best practices that you adhere to when training a model locally?
| 2 |
Any footguns that you try and avoid? Please share your wisdom!
| 2025-05-23T01:01:18 |
https://www.reddit.com/r/LocalLLaMA/comments/1kt70i8/what_are_the_best_practices_that_you_adhere_to/
|
PabloKaskobar
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt70i8
| false | null |
t3_1kt70i8
|
/r/LocalLLaMA/comments/1kt70i8/what_are_the_best_practices_that_you_adhere_to/
| false | false |
self
| 2 | null |
Sonnet 4 dropped… still feels like a 3.7.1 minor release
| 144 |
Curious if anyone's seen big improvements in edge cases or long-context tasks?
| 2025-05-23T01:04:09 |
Odd_Tumbleweed574
|
i.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt72ic
| false | null |
t3_1kt72ic
|
/r/LocalLLaMA/comments/1kt72ic/sonnet_4_dropped_still_feels_like_a_371_minor/
| false | false | 144 |
{'enabled': True, 'images': [{'id': 'xmFWFllgFbuY3CXsgIS3q_PRLP0IhI1vU9mhq2h0YYw', 'resolutions': [{'height': 97, 'url': 'https://preview.redd.it/lambib8skf2f1.png?width=108&crop=smart&auto=webp&s=3293b3b4d47004083eed83b0ceddbbb888924dea', 'width': 108}, {'height': 194, 'url': 'https://preview.redd.it/lambib8skf2f1.png?width=216&crop=smart&auto=webp&s=653fd161f3a72961cc3cb04225f8607660fda452', 'width': 216}, {'height': 287, 'url': 'https://preview.redd.it/lambib8skf2f1.png?width=320&crop=smart&auto=webp&s=ed02962b7d5b03f2b70acde39668b2e2f62d9adc', 'width': 320}, {'height': 575, 'url': 'https://preview.redd.it/lambib8skf2f1.png?width=640&crop=smart&auto=webp&s=74a3f2740d0fc1f938c9b14e4bc5947bc0ce8931', 'width': 640}, {'height': 863, 'url': 'https://preview.redd.it/lambib8skf2f1.png?width=960&crop=smart&auto=webp&s=b19de179b804ab845f09473e54a623184614292f', 'width': 960}, {'height': 970, 'url': 'https://preview.redd.it/lambib8skf2f1.png?width=1080&crop=smart&auto=webp&s=806304b2130d342eb295920e17cc0893a88abee0', 'width': 1080}], 'source': {'height': 1424, 'url': 'https://preview.redd.it/lambib8skf2f1.png?auto=webp&s=930b807944bf434039cb71e1d9bbc150aae742ad', 'width': 1584}, 'variants': {}}]}
|
||
Did Anthropic drop Claude 3.7’s best GPQA score in the new chart?
| 82 |
Claude 3.7 used to show **84.8%** on GPQA with extended thinking.
Now in the new chart, it only shows **78.2%** — the non-extended score — while Claude 4 gets to show its extended scores (83.3%, 83.8%).
So... the 3.7 number went down, the 4 numbers went up. 🤔
Did they quietly change the comparison to make the upgrade look bigger?
Maybe I'm missing some detail from the announcement blog.
| 2025-05-23T01:19:30 |
https://www.reddit.com/gallery/1kt7cy7
|
Odd_Tumbleweed574
|
reddit.com
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt7cy7
| false | null |
t3_1kt7cy7
|
/r/LocalLLaMA/comments/1kt7cy7/did_anthropic_drop_claude_37s_best_gpqa_score_in/
| false | false | 82 | null |
|
BTW: If you are getting a single GPU, VRAM is not the only thing that matters
| 60 |
For example, if you have a 5060 Ti 16GB or an RX 9070 XT 16GB and use Qwen 3 30b-a3b q4_k_m with 16k context, you will likely overflow around 8.5GB to system memory. Assuming you do not do CPU offloading, that load now runs squarely on PCIE bandwidth and your system RAM speed. PCIE 5 x16 on the RX 9070 XT is going to help you a lot in feeding that GPU compared to the PCIE 5 x8 available on the 5060 Ti, resulting in much faster tokens per second for the 9070 XT, and making CPU offloading unnecessary in this scenario, whereas the 5060 Ti will become heavily bottlenecked.
While I returned my 5060 Ti for a 9070 XT and didn't get numbers for the former, I did see 42 t/s while the VRAM was overloaded to this degree on the Vulkan backend. Also, AMD does Vulkan way better then Nvidia, as Nvidia tends to crash when using Vulkan.
| 2025-05-23T01:44:05 |
https://www.reddit.com/r/LocalLLaMA/comments/1kt7u1n/btw_if_you_are_getting_a_single_gpu_vram_is_not/
|
pneuny
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt7u1n
| false | null |
t3_1kt7u1n
|
/r/LocalLLaMA/comments/1kt7u1n/btw_if_you_are_getting_a_single_gpu_vram_is_not/
| false | false |
self
| 60 | null |
AGI Coming Soon... after we master 2nd grade math
| 168 |
[Claude 4 Sonnet](https://preview.redd.it/pe2eeljssf2f1.png?width=580&format=png&auto=webp&s=f881b7ce4409013458c17fff08e8377a329cb9df)
When will LLM master the classic "9.9 - 9.11" problem???
| 2025-05-23T01:47:36 |
https://www.reddit.com/r/LocalLLaMA/comments/1kt7whv/agi_coming_soon_after_we_master_2nd_grade_math/
|
SingularitySoooon
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt7whv
| false | null |
t3_1kt7whv
|
/r/LocalLLaMA/comments/1kt7whv/agi_coming_soon_after_we_master_2nd_grade_math/
| false | false | 168 | null |
|
Anyone using 'PropertyGraphIndex' from Llama Index in production?
| 0 |
Hey folks
I'm wondering if anyone here has experience using LlamaIndex’s `PropertyGraphIndex` for production graph retrieval?
I’m currently building a hybrid retrieval system for my company using Llama Index. I’ve had no issues setting up and querying vector indexes (really solid there), but working with the graph side of things has been rough.
Specifically:
* Instantiating a `PropertyGraphIndex` from nodes/documents is *painfully* slow. I’m working with a small dataset (\~2,000 nodes) and it takes over **2 hours** to build the graph. That feels way too long and doesn’t seem like it would scale at all. (Yes, I know there are parallelism knobs to tweak - but still.)
* Updating the graph dynamically (i.e., inserting new nodes or relations) has been even worse. I can’t get relation updates to persist properly when saving the index.
Curious -has anyone gotten this to work cleanly in production? If not, what graph retrieval stack are you using instead?
Would love to hear what’s working (or not) for others.
| 2025-05-23T01:47:42 |
https://www.reddit.com/r/LocalLLaMA/comments/1kt7wke/anyone_using_propertygraphindex_from_llama_index/
|
l0gr1thm1k
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt7wke
| false | null |
t3_1kt7wke
|
/r/LocalLLaMA/comments/1kt7wke/anyone_using_propertygraphindex_from_llama_index/
| false | false |
self
| 0 | null |
🎙️ Offline Speech-to-Text with NVIDIA Parakeet-TDT 0.6B v2
| 1 |
[removed]
| 2025-05-23T02:07:02 |
https://www.reddit.com/r/LocalLLaMA/comments/1kt8a10/offline_speechtotext_with_nvidia_parakeettdt_06b/
|
srireddit2020
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt8a10
| false | null |
t3_1kt8a10
|
/r/LocalLLaMA/comments/1kt8a10/offline_speechtotext_with_nvidia_parakeettdt_06b/
| false | false | 1 |
{'enabled': False, 'images': [{'id': 'PrxhDh6SmcLcUZ54sXLyejHndv-QociEgKr1_efW9FE', 'resolutions': [{'height': 72, 'url': 'https://external-preview.redd.it/YRkD_4f9GG3JjS7U-VyOMhD6UqAgTs9g61YUbxvrlqk.jpg?width=108&crop=smart&auto=webp&s=4d30f91364c95fc36334e172e3ca8303d977ae80', 'width': 108}, {'height': 144, 'url': 'https://external-preview.redd.it/YRkD_4f9GG3JjS7U-VyOMhD6UqAgTs9g61YUbxvrlqk.jpg?width=216&crop=smart&auto=webp&s=ccd48a1a6d08f0470b2e5adf58dee82ba74a1340', 'width': 216}, {'height': 213, 'url': 'https://external-preview.redd.it/YRkD_4f9GG3JjS7U-VyOMhD6UqAgTs9g61YUbxvrlqk.jpg?width=320&crop=smart&auto=webp&s=c9808d0e7ecfc24a260183cd25a9f2597032be9a', 'width': 320}, {'height': 426, 'url': 'https://external-preview.redd.it/YRkD_4f9GG3JjS7U-VyOMhD6UqAgTs9g61YUbxvrlqk.jpg?width=640&crop=smart&auto=webp&s=8b248daf592d1e451e027b35573c081cecc63696', 'width': 640}, {'height': 640, 'url': 'https://external-preview.redd.it/YRkD_4f9GG3JjS7U-VyOMhD6UqAgTs9g61YUbxvrlqk.jpg?width=960&crop=smart&auto=webp&s=bfc6cf1092ee57c1c48eb737b59f66a117878ce6', 'width': 960}, {'height': 720, 'url': 'https://external-preview.redd.it/YRkD_4f9GG3JjS7U-VyOMhD6UqAgTs9g61YUbxvrlqk.jpg?width=1080&crop=smart&auto=webp&s=701716d04aba28e435acc2447ccad345217fb23b', 'width': 1080}], 'source': {'height': 800, 'url': 'https://external-preview.redd.it/YRkD_4f9GG3JjS7U-VyOMhD6UqAgTs9g61YUbxvrlqk.jpg?auto=webp&s=89b25f531f3dab0ae5c3ccd852cd10215b74883d', 'width': 1200}, 'variants': {}}]}
|
|
GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement Learning
| 9 |
||
||
|**GoT-R1-1B**|[🤗 HuggingFace](https://huggingface.co/gogoduan/GoT-R1-1B)|
|**GoT-R1-7B**|[🤗 HuggingFace](https://huggingface.co/gogoduan/GoT-R1-7B)|
| 2025-05-23T02:58:58 |
https://arxiv.org/abs/2505.17022
|
ninjasaid13
|
arxiv.org
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt9903
| false | null |
t3_1kt9903
|
/r/LocalLLaMA/comments/1kt9903/gotr1_unleashing_reasoning_capability_of_mllm_for/
| false | false |
default
| 9 | null |
Building a real-world LLM agent with open-source models—structure > prompt engineering
| 19 |
I have been working on a production LLM agent the past couple months. Customer support use case with structured workflows like cancellations, refunds, and basic troubleshooting. After lots of playing with open models (Mistral, LLaMA, etc.), this is the first time it feels like the agent is reliable and not just a fancy demo.
Started out with a typical RAG + prompt stack (LangChain-style), but it wasn’t cutting it. The agent would drift from instructions, invent things, or break tone consistency. Spent a ton of time tweaking prompts just to handle edge cases, and even then, things broke in weird ways.
What finally clicked was leaning into a more structured approach using a modeling framework called Parlant where I could define behavior in small, testable units instead of stuffing everything into a giant system prompt. That made it way easier to trace why things were going wrong and fix specific behaviors without destabilizing the rest.
Now the agent handles multi-turn flows cleanly, respects business rules, and behaves predictably even when users go off the happy path. Success rate across 80+ intents is north of 90%, with minimal hallucination.
This is only the beginning so wish me luck
| 2025-05-23T02:59:41 |
https://www.reddit.com/r/LocalLLaMA/comments/1kt99hi/building_a_realworld_llm_agent_with_opensource/
|
Ecstatic-Cranberry90
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt99hi
| false | null |
t3_1kt99hi
|
/r/LocalLLaMA/comments/1kt99hi/building_a_realworld_llm_agent_with_opensource/
| false | false |
self
| 19 | null |
How do I generate .mmproj file?
| 2 |
I can generate GGUFs with llama.cpp but how do I make the mmproj file for multimodal support?
| 2025-05-23T03:16:56 |
https://www.reddit.com/r/LocalLLaMA/comments/1kt9ky1/how_do_i_generate_mmproj_file/
|
HornyGooner4401
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt9ky1
| false | null |
t3_1kt9ky1
|
/r/LocalLLaMA/comments/1kt9ky1/how_do_i_generate_mmproj_file/
| false | false |
self
| 2 | null |
A per-project memory feature for local models?
| 1 |
Some local models, like Qwen3-30B, are still struggling with long multi-turn conversations.
So a per-project or per-conversation memory feature, such as automatically generated bullet-point summaries of the entire conversation, then feed it back to the LLM, maybe would help them maintain context?
| 2025-05-23T03:17:33 |
AaronFeng47
|
i.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt9lax
| false | null |
t3_1kt9lax
|
/r/LocalLLaMA/comments/1kt9lax/a_perproject_memory_feature_for_local_models/
| false | false | 1 |
{'enabled': True, 'images': [{'id': 'qAxhwL3ZmEl1eH2rgYLjV9GF7oJVSfoPFFVrFT7q2vQ', 'resolutions': [{'height': 177, 'url': 'https://preview.redd.it/4pvxf2uz8g2f1.png?width=108&crop=smart&auto=webp&s=d9cf7c382763f4edc39875f4acc81c9a5dfd20f4', 'width': 108}, {'height': 354, 'url': 'https://preview.redd.it/4pvxf2uz8g2f1.png?width=216&crop=smart&auto=webp&s=ea6e76ad886120cd2d1c9b018402cb60df4e9b81', 'width': 216}, {'height': 524, 'url': 'https://preview.redd.it/4pvxf2uz8g2f1.png?width=320&crop=smart&auto=webp&s=f0888d69985e695a64d78fa4d45e953c8a779e62', 'width': 320}, {'height': 1048, 'url': 'https://preview.redd.it/4pvxf2uz8g2f1.png?width=640&crop=smart&auto=webp&s=10343bcb444e43e27068c1a2736688e0ca859010', 'width': 640}, {'height': 1573, 'url': 'https://preview.redd.it/4pvxf2uz8g2f1.png?width=960&crop=smart&auto=webp&s=af95cc8da1e758fd9340ca2ea933d766a2f8c6be', 'width': 960}, {'height': 1770, 'url': 'https://preview.redd.it/4pvxf2uz8g2f1.png?width=1080&crop=smart&auto=webp&s=5165f8338c6c595dd2d4793c9ec90969f51ab876', 'width': 1080}], 'source': {'height': 1770, 'url': 'https://preview.redd.it/4pvxf2uz8g2f1.png?auto=webp&s=80c8eaf377efd169e3f59ab18d108243a94ebe8c', 'width': 1080}, 'variants': {}}]}
|
||
I accidentally too many P100
| 1 |
[removed]
| 2025-05-23T03:18:56 |
https://www.reddit.com/gallery/1kt9m7h
|
TooManyPascals
|
reddit.com
| 1970-01-01T00:00:00 | 0 |
{}
|
1kt9m7h
| false | null |
t3_1kt9m7h
|
/r/LocalLLaMA/comments/1kt9m7h/i_accidentally_too_many_p100/
| false | false | 1 | null |
|
Is Claude 4 worse than 3.7 for anyone else?
| 38 |
I know, I know, whenever a model comes out you get people saying this, but it's on very concrete things for me, I'm not just biased against it. For reference, I'm comparing 4 Sonnet (concise) with 3.7 Sonnet (concise), no reasoning for either.
I asked it to calculate the total markup I paid at a gas station relative to the supermarket. I gave it quantities in a way I thought was clear ("I got three protein bars and three milks, one of the others each. What was the total markup I paid?", but that's later in the conversation after it searched for prices). And indeed, 3.7 understands this without any issue (and I regenerated the message to make sure it wasn't a fluke). But with 4, even with much back and forth and several regenerations, it kept interpreting this as 3 milk, 1 protein bar, 1 [other item], 1 [other item], until I very explicitly laid it out as I just did.
And then, another conversation, I ask it, "Does this seem correct, or too much?" with a photo of food, and macro estimates for the meal in a screenshot. Again, 3.7 understands this fine, as asking whether the figures seem to be an accurate estimate. Whereas 4, again with a couple regenerations to test, seems to think I'm asking whether it's an appropriate meal (as in, not too much food for dinner or whatever). And in one instance, misreads the screenshot (thinking that the number of calories I will have cumulatively eaten after that meal is the number of calories _of_ that meal).
Is anyone else seeing any issues like this?
| 2025-05-23T03:45:40 |
https://www.reddit.com/r/LocalLLaMA/comments/1kta3re/is_claude_4_worse_than_37_for_anyone_else/
|
TrekkiMonstr
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kta3re
| false | null |
t3_1kta3re
|
/r/LocalLLaMA/comments/1kta3re/is_claude_4_worse_than_37_for_anyone_else/
| false | false |
self
| 38 | null |
How to get the most out of my AMD 7900XT?
| 18 |
I was forced to sell my Nvidia 4090 24GB this week to pay rent 😭. I didn't know you could be so emotionally attached to a video card.
Anyway, my brother lent me his 7900XT until his rig is ready. I was just getting into local AI and want to continue. I've heard AMD is hard to support.
Can anyone help get me started on the right foot and advise what I need to get the most out this card?
Specs
- Windows 11 Pro 64bit
- AMD 7800X3D
- AMD 7900XT 20GB
- 32GB DDR5
Previously installed tools
- Ollama
- LM Studio
| 2025-05-23T03:57:34 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktabgk/how_to_get_the_most_out_of_my_amd_7900xt/
|
crispyfrybits
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktabgk
| false | null |
t3_1ktabgk
|
/r/LocalLLaMA/comments/1ktabgk/how_to_get_the_most_out_of_my_amd_7900xt/
| false | false |
self
| 18 | null |
Anyone using MedGemma 27B?
| 11 |
I noticed MedGemma 27B is text-only, instruction-tuned (for inference-time compute), while 4B is the multimodal version. Interesting decision by Google.
| 2025-05-23T04:00:23 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktad7a/anyone_using_medgemma_27b/
|
DeGreiff
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktad7a
| false | null |
t3_1ktad7a
|
/r/LocalLLaMA/comments/1ktad7a/anyone_using_medgemma_27b/
| false | false |
self
| 11 | null |
Big base models? (Not instruct tuned)
| 10 |
I was disappointed to see that Qwen3 didn't release base models for anything over 30b.
Sucks because QLoRa fine tuning is affordable even on 100b+ models.
What are the best large open base models we have right now?
| 2025-05-23T04:25:39 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktat5b/big_base_models_not_instruct_tuned/
|
RedditAddict6942O
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktat5b
| false | null |
t3_1ktat5b
|
/r/LocalLLaMA/comments/1ktat5b/big_base_models_not_instruct_tuned/
| false | false |
self
| 10 | null |
Soon.
| 0 | 2025-05-23T04:44:57 |
New_Alps_5655
|
i.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktb4jh
| false | null |
t3_1ktb4jh
|
/r/LocalLLaMA/comments/1ktb4jh/soon/
| false | false | 0 |
{'enabled': True, 'images': [{'id': 'nXGZefXH-wQJkwnKjGWJLorQPQ2gry0FtX02p08r2KA', 'resolutions': [{'height': 108, 'url': 'https://preview.redd.it/les4pl4kog2f1.png?width=108&crop=smart&auto=webp&s=0eb6b7e739aab5f99186bc6642c00aa9dbe6539a', 'width': 108}, {'height': 216, 'url': 'https://preview.redd.it/les4pl4kog2f1.png?width=216&crop=smart&auto=webp&s=46e054adc22693d888ee6754e3f0154c90d95605', 'width': 216}, {'height': 320, 'url': 'https://preview.redd.it/les4pl4kog2f1.png?width=320&crop=smart&auto=webp&s=a866ea209c7abd2f6081ce7dda0e964f9b3641e2', 'width': 320}, {'height': 640, 'url': 'https://preview.redd.it/les4pl4kog2f1.png?width=640&crop=smart&auto=webp&s=a52639d907a65b4fc4b3c227280247a9fca7a262', 'width': 640}, {'height': 960, 'url': 'https://preview.redd.it/les4pl4kog2f1.png?width=960&crop=smart&auto=webp&s=477f69dd21946c73ac59ed92d867f3625c350388', 'width': 960}], 'source': {'height': 1024, 'url': 'https://preview.redd.it/les4pl4kog2f1.png?auto=webp&s=047cf5c7d571c4bc49f2b9e27db7163a0e266e9e', 'width': 1024}, 'variants': {}}]}
|
|||
Best nsfw open source model for text/image to video on a 4090?
| 1 |
[removed]
| 2025-05-23T04:54:41 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktba61/best_nsfw_open_source_model_for_textimage_to/
|
drowning_in_taxbills
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktba61
| false | null |
t3_1ktba61
|
/r/LocalLLaMA/comments/1ktba61/best_nsfw_open_source_model_for_textimage_to/
| false | false |
nsfw
| 1 | null |
Dans-PersonalityEngine V1.3.0 12b & 24b
| 50 |
The latest release in the Dans-PersonalityEngine series. With any luck you should find it to be an improvement on almost all fronts as compared to V1.2.0.
[https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.3.0-12b](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.3.0-12b)
[https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.3.0-24b](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.3.0-24b)
A blog post regarding its development can be found [here](https://pocketdoclabs.com/making-dans-personalityengine-v130/) for those interested in some rough technical details on the project.
| 2025-05-23T04:55:30 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktban0/danspersonalityengine_v130_12b_24b/
|
PocketDocLabs
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktban0
| false | null |
t3_1ktban0
|
/r/LocalLLaMA/comments/1ktban0/danspersonalityengine_v130_12b_24b/
| false | false |
self
| 50 |
{'enabled': False, 'images': [{'id': 'ArS_gNtL-OdIhiI1BvYfvsPQ6mNyB6F2FtC0KwMgPgA', 'resolutions': [{'height': 109, 'url': 'https://external-preview.redd.it/aHyVm1T1KjGsXPKqm5U-JAWbC_lrL8H6OKIWKYa-iQI.jpg?width=108&crop=smart&auto=webp&s=a76e6de19629152930d0028a563d2fd67085b181', 'width': 108}, {'height': 218, 'url': 'https://external-preview.redd.it/aHyVm1T1KjGsXPKqm5U-JAWbC_lrL8H6OKIWKYa-iQI.jpg?width=216&crop=smart&auto=webp&s=50876de2fcf44aa51ca4e6677f1a6a5144c9d766', 'width': 216}, {'height': 323, 'url': 'https://external-preview.redd.it/aHyVm1T1KjGsXPKqm5U-JAWbC_lrL8H6OKIWKYa-iQI.jpg?width=320&crop=smart&auto=webp&s=6929177e6330a6df8c64aea1e3eb273bb21414c1', 'width': 320}], 'source': {'height': 388, 'url': 'https://external-preview.redd.it/aHyVm1T1KjGsXPKqm5U-JAWbC_lrL8H6OKIWKYa-iQI.jpg?auto=webp&s=fb51b6d9414bdfbbe469717045f7da6159ac91d1', 'width': 384}, 'variants': {}}]}
|
How well do AI models perform on everyday image editing tasks? Not super well, apparently — but according to this new paper, they can already handle around one-third of all requests.
| 4 | 2025-05-23T04:55:42 |
https://arxiv.org/abs/2505.16181
|
taesiri
|
arxiv.org
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktbar2
| false | null |
t3_1ktbar2
|
/r/LocalLLaMA/comments/1ktbar2/how_well_do_ai_models_perform_on_everyday_image/
| false | false |
default
| 4 | null |
|
Choosing between M4 Air or PC with RTX 5060 TI 16GB
| 1 |
Hey!
I intend to start using Local LLMs for programming. Right now I have to choose between one of the following options.
1. Upgrade from MacBook Air 2020 to MacBook Air 2025 M4 with 32 GB RAM
2. Get RTX 5060TI 16 Gb for an existing PC with 32GB RAM and Core i3 12th gen
In terms of speed, who will outperform. Remember I just want to run models. No training.
Thanks.
| 2025-05-23T05:19:00 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktbofl/choosing_between_m4_air_or_pc_with_rtx_5060_ti/
|
engineerhead
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktbofl
| false | null |
t3_1ktbofl
|
/r/LocalLLaMA/comments/1ktbofl/choosing_between_m4_air_or_pc_with_rtx_5060_ti/
| false | false |
self
| 1 | null |
[New paper] Scaling law for quantization-aware training. Is it still possible for bitnet？
| 1 |
[removed]
| 2025-05-23T05:19:44 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktboun/new_paper_scaling_law_for_quantizationaware/
|
Delicious-Number-237
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktboun
| false | null |
t3_1ktboun
|
/r/LocalLLaMA/comments/1ktboun/new_paper_scaling_law_for_quantizationaware/
| false | false |
self
| 1 | null |
Hardware Suggestions for Local AI
| 1 |
I am hoping to go with this combo ryzen 5 7600 b650 16gb ram Rtx 5060ti. Should I jumping to 7 7600? Purpose R&D local diffusion and LLMs?
| 2025-05-23T05:23:20 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktbqtu/hardware_suggestions_for_local_ai/
|
OkBother4153
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktbqtu
| false | null |
t3_1ktbqtu
|
/r/LocalLLaMA/comments/1ktbqtu/hardware_suggestions_for_local_ai/
| false | false |
self
| 1 | null |
Claude 4's SWE-bench scores look overly bloated. How to check for myself?
| 1 |
[removed]
| 2025-05-23T05:32:44 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktbvzl/claude_4s_swebench_scores_look_overly_bloated_how/
|
sirjuicymango
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktbvzl
| false | null |
t3_1ktbvzl
|
/r/LocalLLaMA/comments/1ktbvzl/claude_4s_swebench_scores_look_overly_bloated_how/
| false | false |
self
| 1 | null |
Is there an easier way to search huggingface?! looking for large gguf models!
| 3 |
My friends, I have been out of the loop for a while, I'm still using Behemoth 123b V1 for creative writing. I imagine there are newer, shinier and maybe better models out there but i can't seem to "find" them.
Is there a way to search huggingface for let's say... >100B gguf models?
I'll would also accept directions towards any popular large models around the 123B range (or larger i guess)
has the large model scene dried up? or did everyone move to some random arbitrary number that's difficult to find like 117B or something lol
anyways, thank you for your time :)
| 2025-05-23T05:34:40 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktbx27/is_there_an_easier_way_to_search_huggingface/
|
DominicanGreg
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktbx27
| false | null |
t3_1ktbx27
|
/r/LocalLLaMA/comments/1ktbx27/is_there_an_easier_way_to_search_huggingface/
| false | false |
self
| 3 | null |
NovelSeek: When Agent Becomes the Scientist -- Building Closed-Loop System from Hypothesis to Verification
| 0 |
rtificial Intelligence (AI) is accelerating the transformation of scientific research paradigms, not only enhancing research efficiency but also driving innovation. We introduce NovelSeek, a unified closed-loop multi-agent framework to conduct Autonomous Scientific Research (ASR) across various scientific research fields, enabling researchers to tackle complicated problems in these fields with unprecedented speed and precision. NovelSeek highlights three key advantages:
1) Scalability: NovelSeek has demonstrated its versatility across 12 scientific research tasks, capable of generating innovative ideas to enhance the performance of baseline code.
2) Interactivity: NovelSeek provides an interface for human expert feedback and multi-agent interaction in automated end-to-end processes, allowing for the seamless integration of domain expert knowledge.
3) Efficiency: NovelSeek has achieved promising performance gains in several scientific fields with significantly less time cost compared to human efforts. For instance, in reaction yield prediction, it increased from 27.6% to 35.4% in just 12 hours; in enhancer activity prediction, accuracy rose from 0.52 to 0.79 with only 4 hours of processing; and in 2D semantic segmentation, precision advanced from 78.8% to 81.0% in a mere 30 hours.
| 2025-05-23T05:45:09 |
https://arxiv.org/pdf/2505.16938
|
Lynncc6
|
arxiv.org
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktc2rf
| false | null |
t3_1ktc2rf
|
/r/LocalLLaMA/comments/1ktc2rf/novelseek_when_agent_becomes_the_scientist/
| false | false |
default
| 0 | null |
Need help in retrieving using llm
| 1 |
[removed]
| 2025-05-23T05:45:32 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktc2ys/need_help_in_retrieving_using_llm/
|
420Deku
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktc2ys
| false | null |
t3_1ktc2ys
|
/r/LocalLLaMA/comments/1ktc2ys/need_help_in_retrieving_using_llm/
| false | false |
self
| 1 | null |
Anthropic's new AI model turns to blackmail when engineers try to take it offline | TechCrunch
| 0 |
I'll admit this made me laugh.
| 2025-05-23T06:30:02 |
https://techcrunch.com/2025/05/22/anthropics-new-ai-model-turns-to-blackmail-when-engineers-try-to-take-it-offline/
|
mustafar0111
|
techcrunch.com
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktcqub
| false | null |
t3_1ktcqub
|
/r/LocalLLaMA/comments/1ktcqub/anthropics_new_ai_model_turns_to_blackmail_when/
| false | false | 0 |
{'enabled': False, 'images': [{'id': 'J0ij2SxhpJStUsBOFXmzOsVBoTLP-rqjWbskNZUUgNA', 'resolutions': [{'height': 81, 'url': 'https://external-preview.redd.it/0yOKGorR19ARamoNt8dEySsZD2Mkb_pGmPpDif9aLvY.jpg?width=108&crop=smart&auto=webp&s=7448913aa0e774ccf26c9b14e612cba557f3311f', 'width': 108}, {'height': 162, 'url': 'https://external-preview.redd.it/0yOKGorR19ARamoNt8dEySsZD2Mkb_pGmPpDif9aLvY.jpg?width=216&crop=smart&auto=webp&s=2ef6b9458626aa64d8f9b66f5f29fd373a819fdf', 'width': 216}, {'height': 240, 'url': 'https://external-preview.redd.it/0yOKGorR19ARamoNt8dEySsZD2Mkb_pGmPpDif9aLvY.jpg?width=320&crop=smart&auto=webp&s=e102837f77d719b7e48ded7be2534a341ab68500', 'width': 320}, {'height': 480, 'url': 'https://external-preview.redd.it/0yOKGorR19ARamoNt8dEySsZD2Mkb_pGmPpDif9aLvY.jpg?width=640&crop=smart&auto=webp&s=d9f28d913dc792453f5ff54fb69417cfdf430b11', 'width': 640}, {'height': 720, 'url': 'https://external-preview.redd.it/0yOKGorR19ARamoNt8dEySsZD2Mkb_pGmPpDif9aLvY.jpg?width=960&crop=smart&auto=webp&s=5d492cbfa2f4aff0c9bbf022a402909f66fcf096', 'width': 960}, {'height': 810, 'url': 'https://external-preview.redd.it/0yOKGorR19ARamoNt8dEySsZD2Mkb_pGmPpDif9aLvY.jpg?width=1080&crop=smart&auto=webp&s=9b0b47492bf5dae4263bf2e2546cbaa4dbe8dd60', 'width': 1080}], 'source': {'height': 900, 'url': 'https://external-preview.redd.it/0yOKGorR19ARamoNt8dEySsZD2Mkb_pGmPpDif9aLvY.jpg?auto=webp&s=c86290c2f6bf3f220c0eb422290631fbdfdf4b80', 'width': 1200}, 'variants': {}}]}
|
|
Upgrade path recommendation needed
| 0 |
I am a mere peasant and I have finite budgets of at most $4,000 USD. I am thinking about adding two more 3090s but afraid that bandwidth from 4.0 x4 would limit single GPU performance on small models like Qwen3 32B when being fed with prompts continuously. Been thinking about upgrading CPU side (currently 5600X + DDR4 3200 32GB) to a 5th gen WRX80 or 9175F and possibly try out CPU only inference. I am able to find a deal on the 9175F for \~$2,100, and my local used 3090s are selling at around $750+ each. What should I do for upgrade?
| 2025-05-23T06:30:50 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktcral/upgrade_path_recommendation_needed/
|
m31317015
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktcral
| false | null |
t3_1ktcral
|
/r/LocalLLaMA/comments/1ktcral/upgrade_path_recommendation_needed/
| false | false |
self
| 0 | null |
2x5090 vs. Mac Studio M3 Ultra for concurrent users (help)
| 1 |
[removed]
| 2025-05-23T06:42:25 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktcxls/2x5090_vs_mac_studio_m3_ultra_for_concurrent/
|
Jarlsvanoid
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktcxls
| false | null |
t3_1ktcxls
|
/r/LocalLLaMA/comments/1ktcxls/2x5090_vs_mac_studio_m3_ultra_for_concurrent/
| false | false |
self
| 1 | null |
Compatibility
| 1 |
[removed]
| 2025-05-23T06:48:23 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktd0oc/compatibility/
|
666WhTr666
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktd0oc
| false | null |
t3_1ktd0oc
|
/r/LocalLLaMA/comments/1ktd0oc/compatibility/
| false | false |
self
| 1 | null |
Ollama 0.7.0 taking much longer as 0.6.8. Or is it just me?
| 2 |
I know they have a new engine, its just so jarring how much longer things are taking. I have a crappy setup with a 1660ti, using gemma3:4b and Home Assistant/Frigate, but still. Things that were taking 13 seconds are now 1.5-2minutes. I feel like i am missing some config that would normalize this, or I should just switch to llamacpp. All i wanted to do was try out qwen2.5vl.
| 2025-05-23T06:58:40 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktd5w6/ollama_070_taking_much_longer_as_068_or_is_it/
|
enoquelights
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktd5w6
| false | null |
t3_1ktd5w6
|
/r/LocalLLaMA/comments/1ktd5w6/ollama_070_taking_much_longer_as_068_or_is_it/
| false | false |
self
| 2 | null |
Unable to fix llama-cpp and transformers handling in pyinstaller .exe
| 1 |
[removed]
| 2025-05-23T07:00:42 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktd72f/unable_to_fix_llamacpp_and_transformers_handling/
|
Exotic_Put_8192
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktd72f
| false | null |
t3_1ktd72f
|
/r/LocalLLaMA/comments/1ktd72f/unable_to_fix_llamacpp_and_transformers_handling/
| false | false |
self
| 1 | null |
Troubles with configuring transformers and llama-cpp with pyinstaller
| 0 |
I am attempting to bundle a rag agent into a .exe.
However on usage of the .exe i keep running into the same two problems.
The first initial problem is with locating llama-cpp, which i have fixed.
The second is a recurring error, which i am unable to solve with any resources i've found on existing queries and gpt responses.
FileNotFoundError: [WinError 3] The system cannot find the path specified: 'C:\\Users\\caio\\AppData\\Local\\Temp\\_MEI43162\\transformers\\models\\__init__.pyc'
[PYI-2444:ERROR] Failed to execute script 'frontend' due to unhandled exception!
I looked into my path, and found no \_\_init\_\_.pyc but a \_\_init\_\_.py
I have attempted to solve this by
1. Modifying the spec file (hasn't worked)
# -*- mode: python ; coding: utf-8 -*-
from PyInstaller.utils.hooks import collect_submodules, collect_data_files
import os
import transformers
import sentence_transformers
hiddenimports = collect_submodules('transformers') + collect_submodules('sentence_transformers')
datas = collect_data_files('transformers') + collect_data_files('sentence_transformers')
a = Analysis(
['frontend.py'],
pathex=[],
binaries=[('C:/Users/caio/miniconda3/envs/rag_new_env/Lib/site-packages/llama_cpp/lib/llama.dll', 'llama_cpp/lib')],
datas=datas,
hiddenimports=hiddenimports,
hookspath=[],
hooksconfig={},
runtime_hooks=[],
excludes=[],
noarchive=False,
optimize=0,
)
pyz = PYZ(a.pure)
exe = EXE(
pyz,
a.scripts,
a.binaries,
a.datas,
[],
name='frontend',
debug=False,
bootloader_ignore_signals=False,
strip=False,
upx=True,
upx_exclude=[],
runtime_tmpdir=None,
console=True,
disable_windowed_traceback=False,
argv_emulation=False,
target_arch=None,
codesign_identity=None,
entitlements_file=None,
)
2. Using specific pyinstaller commands that had worked on my previous system. Hasn't worked.
pyinstaller --onefile --add-binary "C:/Users/caio/miniconda3/envs/rag_new_env/Lib/site-packages/llama_cpp/lib/llama.dll;llama_cpp/lib" rag_gui.py
Both attempts that I have provided fixed my llama\_cpp problem but couldn't solve the transformers model.
the path is as so:
C:/Users/caio/miniconda3/envs/rag_new_env/Lib/site-packages
Please help me on how to solve this.
My transformers use is happening only through sentence\_transformers.
| 2025-05-23T07:09:01 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktdbky/troubles_with_configuring_transformers_and/
|
arnab_best
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktdbky
| false | null |
t3_1ktdbky
|
/r/LocalLLaMA/comments/1ktdbky/troubles_with_configuring_transformers_and/
| false | false |
self
| 0 | null |
GitHub - jacklishufan/LaViDa: Official Implementation of LaViDa: :A Large Diffusion Language Model for Multimodal Understanding
| 50 |
Abstract
>Modern Vision-Language Models (VLMs) can solve a wide range of tasks requiring visual reasoning. In real-world scenarios, desirable properties for VLMs include fast inference and controllable generation (e.g., constraining outputs to adhere to a desired format). However, existing autoregressive (AR) VLMs like LLaVA struggle in these aspects. Discrete diffusion models (DMs) offer a promising alternative, enabling parallel decoding for faster inference and bidirectional context for controllable generation through text-infilling. While effective in language-only settings, DMs' potential for multimodal tasks is underexplored. We introduce LaViDa, a family of VLMs built on DMs. We build LaViDa by equipping DMs with a vision encoder and jointly fine-tune the combined parts for multimodal instruction following. To address challenges encountered, LaViDa incorporates novel techniques such as complementary masking for effective training, prefix KV cache for efficient inference, and timestep shifting for high-quality sampling. Experiments show that LaViDa achieves competitive or superior performance to AR VLMs on multi-modal benchmarks such as MMMU, while offering unique advantages of DMs, including flexible speed-quality tradeoff, controllability, and bidirectional reasoning. On COCO captioning, LaViDa surpasses Open-LLaVa-Next-Llama3-8B by +4.1 CIDEr with 1.92x speedup. On bidirectional tasks, it achieves +59% improvement on Constrained Poem Completion. These results demonstrate LaViDa as a strong alternative to AR VLMs. Code and models is available at [https://github.com/jacklishufan/LaViDa](https://github.com/jacklishufan/LaViDa)
| 2025-05-23T07:23:08 |
https://github.com/jacklishufan/LaViDa
|
ninjasaid13
|
github.com
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktdisj
| false | null |
t3_1ktdisj
|
/r/LocalLLaMA/comments/1ktdisj/github_jacklishufanlavida_official_implementation/
| false | false | 50 |
{'enabled': False, 'images': [{'id': 'zXgBoTT8kcnKxIo2YTXAaXT1tsNUVc63YIAVOZCY5dk', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/_qyQ5Nb0aZ0pjIERMz0EBymLna5bhwRL3S2vTvBvqUQ.jpg?width=108&crop=smart&auto=webp&s=f1e2dba52923cde49de20cc8566cf08a0990b869', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/_qyQ5Nb0aZ0pjIERMz0EBymLna5bhwRL3S2vTvBvqUQ.jpg?width=216&crop=smart&auto=webp&s=4ff9d2985898d2e37ba7ddc7932ebf350e25d16b', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/_qyQ5Nb0aZ0pjIERMz0EBymLna5bhwRL3S2vTvBvqUQ.jpg?width=320&crop=smart&auto=webp&s=223c0bca107075c58c354fc6dd9793818aa97a0b', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/_qyQ5Nb0aZ0pjIERMz0EBymLna5bhwRL3S2vTvBvqUQ.jpg?width=640&crop=smart&auto=webp&s=978b5b8d9f71176e70ad9f69cf874f5d01401ac0', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/_qyQ5Nb0aZ0pjIERMz0EBymLna5bhwRL3S2vTvBvqUQ.jpg?width=960&crop=smart&auto=webp&s=321f6dcb722f008e744176fbfaa2a37652b0ae19', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/_qyQ5Nb0aZ0pjIERMz0EBymLna5bhwRL3S2vTvBvqUQ.jpg?width=1080&crop=smart&auto=webp&s=b60e76e8cb39836edbc4229df413582845b728d2', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/_qyQ5Nb0aZ0pjIERMz0EBymLna5bhwRL3S2vTvBvqUQ.jpg?auto=webp&s=94ec064825550a75340668e0af0d2b17573fa8ba', 'width': 1200}, 'variants': {}}]}
|
|
Unfortunately, Claude 4 lags far behind O3 in the anti-fitting benchmark.
| 16 |
[https://llm-benchmark.github.io/](https://llm-benchmark.github.io/)
click the to expand all questions and answers for all models
I did not update the answers to CLAUDE 4 OPUS THINKING on the webpage. I only tried a few major questions (the rest were even more impossible to answer correctly). I only got 0.5 of the 8 questions right, which is not much different from the total errors in C3.7.（If there is significant progress, I will update the page.）
At present, O3 is still far ahead
I guess the secret is that there should be higher quality customized reasoning data sets, which need to be produced by hiring people. Maybe this is the biggest secret.
| 2025-05-23T07:28:50 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktdlqc/unfortunately_claude_4_lags_far_behind_o3_in_the/
|
flysnowbigbig
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktdlqc
| false | null |
t3_1ktdlqc
|
/r/LocalLLaMA/comments/1ktdlqc/unfortunately_claude_4_lags_far_behind_o3_in_the/
| false | false |
self
| 16 | null |
Best TTS for foreign language (train with my own dataset?)
| 1 |
[removed]
| 2025-05-23T07:29:11 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktdlwm/best_tts_for_foreign_language_train_with_my_own/
|
GuidanceOdd4413
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktdlwm
| false | null |
t3_1ktdlwm
|
/r/LocalLLaMA/comments/1ktdlwm/best_tts_for_foreign_language_train_with_my_own/
| false | false |
self
| 1 | null |
Console Game For LLMs
| 1 |
[removed]
| 2025-05-23T07:33:26 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktdo20/console_game_for_llms/
|
hadoopfromscratch
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktdo20
| false | null |
t3_1ktdo20
|
/r/LocalLLaMA/comments/1ktdo20/console_game_for_llms/
| false | false |
self
| 1 | null |
Local Llama on a Corporate Microsoft stack
| 0 |
I'm used to using Linux and running models on vLLM or llama.cpp and then using python to develop the logic and using postgres+pgvector for the datastore.
However, if you have to run this using corporate Microsoft infrastructure (think SharePoint, PowerAutomate, PowerQuery) what tools can I use to script and pull data that is stored in the SharePoints? I'm not expecting good performance, but since there's only 10k documents, I think even using SharePoint lists will be workable. Assume I have API access to an LLM backend.
| 2025-05-23T07:41:10 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktdrxe/local_llama_on_a_corporate_microsoft_stack/
|
DeltaSqueezer
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktdrxe
| false | null |
t3_1ktdrxe
|
/r/LocalLLaMA/comments/1ktdrxe/local_llama_on_a_corporate_microsoft_stack/
| false | false |
self
| 0 | null |
Console Game For LLMs
| 1 |
[removed]
| 2025-05-23T07:45:06 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktdtyx/console_game_for_llms/
|
hadoopfromscratch
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktdtyx
| false | null |
t3_1ktdtyx
|
/r/LocalLLaMA/comments/1ktdtyx/console_game_for_llms/
| false | false |
self
| 1 | null |
Console Game For LLMs
| 1 |
[removed]
| 2025-05-23T07:52:49 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktdxuu/console_game_for_llms/
|
hadoopfromscratch
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktdxuu
| false | null |
t3_1ktdxuu
|
/r/LocalLLaMA/comments/1ktdxuu/console_game_for_llms/
| false | false |
self
| 1 | null |
[Career Advice Needed] What Next in AI? Feeling Stuck and Need Direction
| 2 |
Hey everyone,
I'm currently at a crossroads in my career and could really use some advice from the LLM and multimodal community because it has lots of AI engineers.
A bit about my current background:
Strong background in Deep Learning and Computer Vision, including object detection and segmentation.
Experienced in deploying models using Nvidia DeepStream, ONNX, and TensorRT.
Basic ROS2 experience, primarily for sanity checks during data collection in robotics.
Extensive hands-on experience with Vision Language Models (VLMs) and open-vocabulary models.
Current Dilemma: I'm feeling stuck and unsure about the best next steps to align with industry growth. Specifically:
1. Should I deepen my formal knowledge through an MS in AI/Computer Vision (possibly IIITs in India)?
2. Focus more on deployment, MLOps, and edge inference, which seems to offer strong job security and specialization?
3. Pivot entirely toward LLMs and multimodal VLMs, given the significant funding and rapid industry expansion in this area?
I'd particularly appreciate insights on:
How valuable has it been for you to integrate LLMs with traditional Computer Vision pipelines?
What specific LLM/VLM skills or experiences helped accelerate your career?
Is formal academic training still beneficial at this point, or is hands-on industry experience sufficient?
Any thoughts, experiences, or candid advice would be extremely valuable.
| 2025-05-23T08:06:09 |
https://www.reddit.com/r/LocalLLaMA/comments/1kte4oo/career_advice_needed_what_next_in_ai_feeling/
|
Southern-Bad-6573
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kte4oo
| false | null |
t3_1kte4oo
|
/r/LocalLLaMA/comments/1kte4oo/career_advice_needed_what_next_in_ai_feeling/
| false | false |
self
| 2 | null |
Reminder on the purpose of the Claude 4 models
| 0 |
As per their blog post, these models are created specifically for both agentic coding tasks and agentic tasks in general. Anthropic's goal is to be able to create models that are able to tackle long-horizon tasks in a consistent manner. So if you are using these models outside of agentic tooling (via direct Q&A - e.g. aider/livebench, etc), I would imagine that o3 and 2.5 pro could be right up there near the claude 4 series. Using these models in agentic settings is necessary in order to actually verify the strides made.
That's really all. Overall, it seems like there is a really good sentiment around these models, but I do see some people that might be unaware of anthropic's current north star goals.
| 2025-05-23T08:29:52 |
https://www.reddit.com/r/LocalLLaMA/comments/1kteg81/reminder_on_the_purpose_of_the_claude_4_models/
|
cobalt1137
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kteg81
| false | null |
t3_1kteg81
|
/r/LocalLLaMA/comments/1kteg81/reminder_on_the_purpose_of_the_claude_4_models/
| false | false |
self
| 0 | null |
Want to know your reviews about this 14B model.
| 1 |
[removed]
| 2025-05-23T08:52:53 |
https://www.reddit.com/r/LocalLLaMA/comments/1kterbh/want_to_know_your_reviews_about_this_14b_model/
|
pinpann
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kterbh
| false | null |
t3_1kterbh
|
/r/LocalLLaMA/comments/1kterbh/want_to_know_your_reviews_about_this_14b_model/
| false | false |
self
| 1 |
{'enabled': False, 'images': [{'id': 'oiXxa3AeQjPyS014SfL85mFkAl65CMnweJS5us56xg8', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/_qPpK7H85T65D99K_551HeZaWXqfclob4aYz5EmnQ68.jpg?width=108&crop=smart&auto=webp&s=d49b6159d1fe495c160f658a33ee4ccaafe1e387', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/_qPpK7H85T65D99K_551HeZaWXqfclob4aYz5EmnQ68.jpg?width=216&crop=smart&auto=webp&s=b134a500efd0a5952007aff765d520f8585a06d2', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/_qPpK7H85T65D99K_551HeZaWXqfclob4aYz5EmnQ68.jpg?width=320&crop=smart&auto=webp&s=90d12ec6f6875ae1194f7fac93195a86f5dce7cf', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/_qPpK7H85T65D99K_551HeZaWXqfclob4aYz5EmnQ68.jpg?width=640&crop=smart&auto=webp&s=c4723cfa4b6f2200f28a9aeab50779f4c9ddd206', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/_qPpK7H85T65D99K_551HeZaWXqfclob4aYz5EmnQ68.jpg?width=960&crop=smart&auto=webp&s=9f5feea662097b2e0b6a7fa30b4f7b6765374140', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/_qPpK7H85T65D99K_551HeZaWXqfclob4aYz5EmnQ68.jpg?width=1080&crop=smart&auto=webp&s=0a20cc6c78c6645d4a7987d5503e9ab2aa8e57dd', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/_qPpK7H85T65D99K_551HeZaWXqfclob4aYz5EmnQ68.jpg?auto=webp&s=e6fb60acb35a5d4b1d994ed6035f29519da6073f', 'width': 1200}, 'variants': {}}]}
|
Said he's "developing" AI Agents, but its just basic prompt eng. + PDFs using ChatGPT App. In how many ways can this go wrong?
| 16 |
It's pretty much this. A PM in my company pushed the owner to believe in 4 months we can have that developed and ntegrated in out platform, when his "POC" is just interactioon with chatgpt app by uploading some PDFs and having it reply questions. Not a fancy RAG let alone an agent. Still, he's promissing this can be developed and integrated in 4 months when he understands little of engieering and there's only one engineer in the company able to work on it. Also, the company never released any AI feature or product before.
I just wanna gather a few arguments on how this can go wrong more on the AI side, relying on one closed model like that seems bold.
| 2025-05-23T09:29:37 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktf9o3/said_hes_developing_ai_agents_but_its_just_basic/
|
Melodic_Reality_646
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktf9o3
| false | null |
t3_1ktf9o3
|
/r/LocalLLaMA/comments/1ktf9o3/said_hes_developing_ai_agents_but_its_just_basic/
| false | false |
self
| 16 | null |
Did Google’s ‘Most Secure’ AI Just Fall For a Sneaky Trick?
| 1 |
[removed]
| 2025-05-23T09:31:35 |
https://v.redd.it/0zv7arog2i2f1
|
Fluffy_Sheepherder76
|
v.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktfao4
| false |
{'reddit_video': {'bitrate_kbps': 800, 'dash_url': 'https://v.redd.it/0zv7arog2i2f1/DASHPlaylist.mpd?a=1750584709%2CZjNjZWYzOTQ0MTE3MGRiNDcwN2Q2ODNlN2M4YmEyOGQyYWRlMDIyNTNmY2VjN2UxN2Q4OTA0ZDAzNjQ3OTU2Mg%3D%3D&v=1&f=sd', 'duration': 47, 'fallback_url': 'https://v.redd.it/0zv7arog2i2f1/DASH_360.mp4?source=fallback', 'has_audio': True, 'height': 360, 'hls_url': 'https://v.redd.it/0zv7arog2i2f1/HLSPlaylist.m3u8?a=1750584709%2CZmU0NzhjYmJkODY0ZmY0NTAxYjQyNDU3YjdlYWE5MmNhNTA3NjVlYTA2NmFmMGQyZDE1NDBjNzU1OGE3ZTE4OQ%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/0zv7arog2i2f1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 638}}
|
t3_1ktfao4
|
/r/LocalLLaMA/comments/1ktfao4/did_googles_most_secure_ai_just_fall_for_a_sneaky/
| false | false | 1 |
{'enabled': False, 'images': [{'id': 'am40Y3Fvb2cyaTJmMd7djdlH2hbWeILH_9cStELUSJie2nmstlRGg59DAyDP', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/am40Y3Fvb2cyaTJmMd7djdlH2hbWeILH_9cStELUSJie2nmstlRGg59DAyDP.png?width=108&crop=smart&format=pjpg&auto=webp&s=96388ca8876ca08dcfa6b4f16517bbe764c1d9ce', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/am40Y3Fvb2cyaTJmMd7djdlH2hbWeILH_9cStELUSJie2nmstlRGg59DAyDP.png?width=216&crop=smart&format=pjpg&auto=webp&s=af04fc31a029b088774e8da94630a4fea2b1bbbf', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/am40Y3Fvb2cyaTJmMd7djdlH2hbWeILH_9cStELUSJie2nmstlRGg59DAyDP.png?width=320&crop=smart&format=pjpg&auto=webp&s=fbf855cb1d553a744c3f1fdd7d336a9d10e95c22', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/am40Y3Fvb2cyaTJmMd7djdlH2hbWeILH_9cStELUSJie2nmstlRGg59DAyDP.png?width=640&crop=smart&format=pjpg&auto=webp&s=30a7ddf29f71d70dce71172c41cb8bb5e0d7fb7a', 'width': 640}], 'source': {'height': 478, 'url': 'https://external-preview.redd.it/am40Y3Fvb2cyaTJmMd7djdlH2hbWeILH_9cStELUSJie2nmstlRGg59DAyDP.png?format=pjpg&auto=webp&s=b3c884ad7a662d11292355fc9971289cc762af34', 'width': 848}, 'variants': {}}]}
|
|
Is ‘Secure’ Just a Marketing Word for AI These Days?
| 1 | 2025-05-23T10:09:47 |
https://v.redd.it/brpu78phai2f1
|
Fluffy_Sheepherder76
|
v.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktfv43
| false |
{'reddit_video': {'bitrate_kbps': 5000, 'dash_url': 'https://v.redd.it/brpu78phai2f1/DASHPlaylist.mpd?a=1750587004%2CNzAzYTYyZjMxMDQ2MTMwODFiMzAwYzIxMDZkNWMwNGY1Mzk3YjNkYmRkNDg1MGQ0MDllZmFhOWVmYjFjZDk0Yg%3D%3D&v=1&f=sd', 'duration': 47, 'fallback_url': 'https://v.redd.it/brpu78phai2f1/DASH_1080.mp4?source=fallback', 'has_audio': True, 'height': 1080, 'hls_url': 'https://v.redd.it/brpu78phai2f1/HLSPlaylist.m3u8?a=1750587004%2COTI0ZDhiZGFiNTNlM2ZjOWZjNzQyYTdmODJhZmEwYmZhN2U2MWZjZjI3Mjg1MGEwNTUzZjZkZTA2MTM1YjVhMw%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/brpu78phai2f1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 1920}}
|
t3_1ktfv43
|
/r/LocalLLaMA/comments/1ktfv43/is_secure_just_a_marketing_word_for_ai_these_days/
| false | false | 1 |
{'enabled': False, 'images': [{'id': 'dGc5anppcGhhaTJmMSROJdQEB0P2BMkw2j5lurWKaGpFdbJnju1mhFkU4a7y', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/dGc5anppcGhhaTJmMSROJdQEB0P2BMkw2j5lurWKaGpFdbJnju1mhFkU4a7y.png?width=108&crop=smart&format=pjpg&auto=webp&s=33463d4f9bc639e82157ab1491b605283f569ebc', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/dGc5anppcGhhaTJmMSROJdQEB0P2BMkw2j5lurWKaGpFdbJnju1mhFkU4a7y.png?width=216&crop=smart&format=pjpg&auto=webp&s=46e51715e024b0a65c3c79db1ddbaf694023fa76', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/dGc5anppcGhhaTJmMSROJdQEB0P2BMkw2j5lurWKaGpFdbJnju1mhFkU4a7y.png?width=320&crop=smart&format=pjpg&auto=webp&s=0d0124f3715a598826840943a3b2cdde933447d4', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/dGc5anppcGhhaTJmMSROJdQEB0P2BMkw2j5lurWKaGpFdbJnju1mhFkU4a7y.png?width=640&crop=smart&format=pjpg&auto=webp&s=5c091447bdeaaec22193597530effbb0ec72ba1a', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/dGc5anppcGhhaTJmMSROJdQEB0P2BMkw2j5lurWKaGpFdbJnju1mhFkU4a7y.png?width=960&crop=smart&format=pjpg&auto=webp&s=cb6504e47dea7c85f92cf72aad34938bd800ae11', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/dGc5anppcGhhaTJmMSROJdQEB0P2BMkw2j5lurWKaGpFdbJnju1mhFkU4a7y.png?width=1080&crop=smart&format=pjpg&auto=webp&s=9e8e99d5b954b04872718e5a224c7e2629ee51b6', 'width': 1080}], 'source': {'height': 1080, 'url': 'https://external-preview.redd.it/dGc5anppcGhhaTJmMSROJdQEB0P2BMkw2j5lurWKaGpFdbJnju1mhFkU4a7y.png?format=pjpg&auto=webp&s=5593a22ec37a2f5c9d648f04f40ba82fe6d6cbab', 'width': 1920}, 'variants': {}}]}
|
||
Curious if this is fast: DeepSeek R1 671B on a 48GB-modded RTX4090, pushing 30 tok/sec
| 1 |
[removed]
| 2025-05-23T10:21:40 |
https://www.reddit.com/gallery/1ktg1s1
|
Zima_Space
|
reddit.com
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktg1s1
| false | null |
t3_1ktg1s1
|
/r/LocalLLaMA/comments/1ktg1s1/curious_if_this_is_fast_deepseek_r1_671b_on_a/
| false | false | 1 | null |
|
Your current setup ?
| 10 |
What is your current setup and how much did it cost ?
I’m curious as I don’t know much about such setups , and don’t know how to go about making my own if I wanted to.
| 2025-05-23T11:00:37 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktgo9f/your_current_setup/
|
Basic-Pay-9535
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktgo9f
| false | null |
t3_1ktgo9f
|
/r/LocalLLaMA/comments/1ktgo9f/your_current_setup/
| false | false |
self
| 10 | null |
What API is same level AND cheaper than Anthropic for dealing with large texts?
| 1 |
[removed]
| 2025-05-23T11:01:59 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktgp9h/what_api_is_same_level_and_cheaper_than_anthropic/
|
Complete-Ask-9428
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktgp9h
| false | null |
t3_1ktgp9h
|
/r/LocalLLaMA/comments/1ktgp9h/what_api_is_same_level_and_cheaper_than_anthropic/
| false | false |
self
| 1 | null |
What API is same level AND cheaper than Anthropic for dealing with large texts?
| 1 |
[removed]
| 2025-05-23T11:03:19 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktgq3d/what_api_is_same_level_and_cheaper_than_anthropic/
|
ARAM_player
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktgq3d
| false | null |
t3_1ktgq3d
|
/r/LocalLLaMA/comments/1ktgq3d/what_api_is_same_level_and_cheaper_than_anthropic/
| false | false |
self
| 1 | null |
Local Assistant - Email/Teams/Slack/Drive - why isn’t this a thing?
| 0 |
Firstly apologies if this has been asked and answered - I’ve looked and didn’t find anything super current.
Basically I would think a main use case would be to allow someone to ask ‘what do I need to focus on today?’ And it would review the last couple of weeks emails/teams/slack/calendar and say ‘you have a meeting with *** at 14:00 about *** based on messages and emails you need to make sure you have the Penske file complete - here is a summary of the Penske file as of the latest revision.
I have looked at manually exported json files or Langchain - is that the best that can be done currently?
Any insight, advice, frustrations would be welcome discussion….
| 2025-05-23T11:06:40 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktgs4o/local_assistant_emailteamsslackdrive_why_isnt/
|
Euphoric-Society1412
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktgs4o
| false | null |
t3_1ktgs4o
|
/r/LocalLLaMA/comments/1ktgs4o/local_assistant_emailteamsslackdrive_why_isnt/
| false | false |
self
| 0 | null |
server audio input has been merged into llama.cpp
| 113 | 2025-05-23T11:12:26 |
https://github.com/ggml-org/llama.cpp/pull/13714
|
jacek2023
|
github.com
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktgvoe
| false | null |
t3_1ktgvoe
|
/r/LocalLLaMA/comments/1ktgvoe/server_audio_input_has_been_merged_into_llamacpp/
| false | false | 113 |
{'enabled': False, 'images': [{'id': '025Mp2vchB0j5ZcEYyfyuRkH70ASsrqgrqWm5911cn8', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/w-TAeYFPuT8QOBKphdcEnCVLkPeOPrOjKse263sRyos.jpg?width=108&crop=smart&auto=webp&s=88e31f15e13472971ce9b125f29cf6994d61a942', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/w-TAeYFPuT8QOBKphdcEnCVLkPeOPrOjKse263sRyos.jpg?width=216&crop=smart&auto=webp&s=f81f9d8e6f0d5b3a29cc3bdf88289f889f118e39', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/w-TAeYFPuT8QOBKphdcEnCVLkPeOPrOjKse263sRyos.jpg?width=320&crop=smart&auto=webp&s=5de6a50414eea002ac9b06831c489b4290483a63', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/w-TAeYFPuT8QOBKphdcEnCVLkPeOPrOjKse263sRyos.jpg?width=640&crop=smart&auto=webp&s=d30ef27dc1e8b5fa00168ba96a589759da20990b', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/w-TAeYFPuT8QOBKphdcEnCVLkPeOPrOjKse263sRyos.jpg?width=960&crop=smart&auto=webp&s=243d43541825aea7bce036e14d872112da4720a0', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/w-TAeYFPuT8QOBKphdcEnCVLkPeOPrOjKse263sRyos.jpg?width=1080&crop=smart&auto=webp&s=3afdc6975cbd4fbb94af064a5a8ab17435003286', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/w-TAeYFPuT8QOBKphdcEnCVLkPeOPrOjKse263sRyos.jpg?auto=webp&s=b504ece21a1cd9670eb735d8da5fc6e18dad3514', 'width': 1200}, 'variants': {}}]}
|
||
AMD vs Nvidia LLM inference quality
| 2 |
For those who have compared the same LLM using the same file with the same quant, fully loaded into VRAM.
How do AMD and Nvidia compare ?
Not asking about speed, but response quality.
Even if the response is not exactly the same, how is the response quality ?
Thank You
| 2025-05-23T11:13:10 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktgw6i/amd_vs_nvidia_llm_inference_quality/
|
Ponsky
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktgw6i
| false | null |
t3_1ktgw6i
|
/r/LocalLLaMA/comments/1ktgw6i/amd_vs_nvidia_llm_inference_quality/
| false | false |
self
| 2 | null |
AceReason-Nemotron-14B: Advancing Math and Code Reasoning through Reinforcement Learning
| 69 | 2025-05-23T11:15:59 |
https://huggingface.co/nvidia/AceReason-Nemotron-14B
|
AaronFeng47
|
huggingface.co
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktgxxa
| false | null |
t3_1ktgxxa
|
/r/LocalLLaMA/comments/1ktgxxa/acereasonnemotron14b_advancing_math_and_code/
| false | false | 69 |
{'enabled': False, 'images': [{'id': 'OIO7hLelckHUPc4PUbni6Q7qWcpbryWC8vuINJV19Ns', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/_aQtUZTp2VBwp5MK35YBXI25HOZhHuEgT9O1MgXLN7I.jpg?width=108&crop=smart&auto=webp&s=d6d87c80a808d26223a77bc2adcfaaa091bd7d14', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/_aQtUZTp2VBwp5MK35YBXI25HOZhHuEgT9O1MgXLN7I.jpg?width=216&crop=smart&auto=webp&s=7dbb406c1e1d0f82333c4e3716a91b69c5867ef1', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/_aQtUZTp2VBwp5MK35YBXI25HOZhHuEgT9O1MgXLN7I.jpg?width=320&crop=smart&auto=webp&s=2e238a9aa698b7b87dde93716bc82d23116e86b2', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/_aQtUZTp2VBwp5MK35YBXI25HOZhHuEgT9O1MgXLN7I.jpg?width=640&crop=smart&auto=webp&s=eb4068255f83d79055a6f21dccc859d949b32f54', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/_aQtUZTp2VBwp5MK35YBXI25HOZhHuEgT9O1MgXLN7I.jpg?width=960&crop=smart&auto=webp&s=6b63c8d1e123bf4db486a05a850ced3234dc27d2', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/_aQtUZTp2VBwp5MK35YBXI25HOZhHuEgT9O1MgXLN7I.jpg?width=1080&crop=smart&auto=webp&s=7c61eeef726c44d74291cd216aaae819fea4420a', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/_aQtUZTp2VBwp5MK35YBXI25HOZhHuEgT9O1MgXLN7I.jpg?auto=webp&s=aa700eadc151f423c28eeb1be7fab6ed515d9eb6', 'width': 1200}, 'variants': {}}]}
|
||
GUI RAG that can do an unlimited number of documents, or at least many
| 5 |
Most available LLM GUIs that can execute RAG can only handle 2 or 3 PDFs.
Are the any interfaces that can handle a bigger number ?
Sure, you can merge PDFs, but that’s a quite messy solution
Thank You
| 2025-05-23T11:17:55 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktgz28/gui_rag_that_can_do_an_unlimited_number_of/
|
Ponsky
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktgz28
| false | null |
t3_1ktgz28
|
/r/LocalLLaMA/comments/1ktgz28/gui_rag_that_can_do_an_unlimited_number_of/
| false | false |
self
| 5 | null |
AI Baby Monitor – fully local Video-LLM nanny (beeps when safety rules are violated)
| 1 |
[removed]
| 2025-05-23T11:40:08 |
https://v.redd.it/vrllbcyjqi2f1
|
CheeringCheshireCat
|
v.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1kthdc7
| false |
{'reddit_video': {'bitrate_kbps': 450, 'dash_url': 'https://v.redd.it/vrllbcyjqi2f1/DASHPlaylist.mpd?a=1750592424%2CMDAxYTQ2ZWE4YTNlOGZkNGU3ZWEwMzlhYmJkYzkxZjU0NmRlZmI2MWQ0MGU5YWNmMmYxODdiZTJiYjI2Mzg1Yw%3D%3D&v=1&f=sd', 'duration': 10, 'fallback_url': 'https://v.redd.it/vrllbcyjqi2f1/DASH_270.mp4?source=fallback', 'has_audio': True, 'height': 480, 'hls_url': 'https://v.redd.it/vrllbcyjqi2f1/HLSPlaylist.m3u8?a=1750592424%2CZDUzOTUwYWMzMzY3YTFhZWE5NjhiZjAwMmVlYmI1NTk3MDdlZWMxNDJkMTllYzhlZmI0Y2QzZmVjNzhhNDBhZg%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/vrllbcyjqi2f1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 258}}
|
t3_1kthdc7
|
/r/LocalLLaMA/comments/1kthdc7/ai_baby_monitor_fully_local_videollm_nanny_beeps/
| false | false | 1 |
{'enabled': False, 'images': [{'id': 'Mng3b2ZieWpxaTJmMVMRslQYMYRN8ZJ1qBgR4-LlFEA6jckhHIJ4it6HP21k', 'resolutions': [{'height': 200, 'url': 'https://external-preview.redd.it/Mng3b2ZieWpxaTJmMVMRslQYMYRN8ZJ1qBgR4-LlFEA6jckhHIJ4it6HP21k.png?width=108&crop=smart&format=pjpg&auto=webp&s=30842bfac6d65ae7b4a9a14f783af1dd7b889f79', 'width': 108}, {'height': 400, 'url': 'https://external-preview.redd.it/Mng3b2ZieWpxaTJmMVMRslQYMYRN8ZJ1qBgR4-LlFEA6jckhHIJ4it6HP21k.png?width=216&crop=smart&format=pjpg&auto=webp&s=687e23601e8296faedd9525ad742c64c83d80174', 'width': 216}, {'height': 593, 'url': 'https://external-preview.redd.it/Mng3b2ZieWpxaTJmMVMRslQYMYRN8ZJ1qBgR4-LlFEA6jckhHIJ4it6HP21k.png?width=320&crop=smart&format=pjpg&auto=webp&s=55fc8efe25aca9bf206c61df07860e907ae571bf', 'width': 320}], 'source': {'height': 640, 'url': 'https://external-preview.redd.it/Mng3b2ZieWpxaTJmMVMRslQYMYRN8ZJ1qBgR4-LlFEA6jckhHIJ4it6HP21k.png?format=pjpg&auto=webp&s=67b1a686088a361b64f11e001f8c95c94b67a83b', 'width': 345}, 'variants': {}}]}
|
|
Stacking 2x3090s back to back for inference only - thermals
| 10 |
Is anyone running 2x3090s stacked (no gap) for Llama 70B inference?
If so, how are your temperatures looking when utilizing both cards for inference?
My single 3090 averages around 35-40% load (140 watts) for inference on 32GB 4bit models. Temperatures are around 60 degrees.
So it seems reasonable to me that I could stack 2x3090s right next to each, and have okay thermals provided the load on the cards remains close to or under 40%/140watts.
Thoughts?
| 2025-05-23T11:41:05 |
https://www.reddit.com/r/LocalLLaMA/comments/1kthdzn/stacking_2x3090s_back_to_back_for_inference_only/
|
YouAreRight007
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kthdzn
| false | null |
t3_1kthdzn
|
/r/LocalLLaMA/comments/1kthdzn/stacking_2x3090s_back_to_back_for_inference_only/
| false | false |
self
| 10 | null |
Comparision
| 1 |
[removed]
| 2025-05-23T11:45:32 |
https://www.reddit.com/gallery/1kthguc
|
deepakhero42069
|
reddit.com
| 1970-01-01T00:00:00 | 0 |
{}
|
1kthguc
| false | null |
t3_1kthguc
|
/r/LocalLLaMA/comments/1kthguc/comparision/
| false | false | 1 | null |
|
Any drawbacks with putting a high end GPU together with a weak GPU on the same system?
| 6 |
Say one of them supports PCIe 5.0 x16 while the other is PCIe 5.0 x8 or even PCIe 4.0, and installed to appropriate PCIe slots that are not lower than the GPU (in terms of PCIe support)
I vaguely recall we cannot mix memory sticks with different clock speeds, but not sure how this works for GPUs
| 2025-05-23T11:49:12 |
https://www.reddit.com/r/LocalLLaMA/comments/1kthj8j/any_drawbacks_with_putting_a_high_end_gpu/
|
prusswan
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kthj8j
| false | null |
t3_1kthj8j
|
/r/LocalLLaMA/comments/1kthj8j/any_drawbacks_with_putting_a_high_end_gpu/
| false | false |
self
| 6 | null |
Llama.cpp is seriously slow. (WSL/5090)
| 1 |
[removed]
| 2025-05-23T12:02:35 |
https://www.reddit.com/r/LocalLLaMA/comments/1kthsn0/llamacpp_is_seriously_slow_wsl5090/
|
Silent_Huckleberry89
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kthsn0
| false | null |
t3_1kthsn0
|
/r/LocalLLaMA/comments/1kthsn0/llamacpp_is_seriously_slow_wsl5090/
| false | false |
self
| 1 | null |
llama.cpp is disastrously slow on GPU
| 1 |
[removed]
| 2025-05-23T12:11:08 |
https://www.reddit.com/r/LocalLLaMA/comments/1kthyug/llamacpp_is_disastrously_slow_on_gpu/
|
indepalt
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kthyug
| false | null |
t3_1kthyug
|
/r/LocalLLaMA/comments/1kthyug/llamacpp_is_disastrously_slow_on_gpu/
| false | false |
self
| 1 | null |
Which Mac would be better to run a 70+ LLM & RAG?
| 1 |
[removed]
| 2025-05-23T12:19:34 |
https://www.reddit.com/r/LocalLLaMA/comments/1kti4xq/which_mac_would_be_better_to_run_a_70_llm_rag/
|
Web3Vortex
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kti4xq
| false | null |
t3_1kti4xq
|
/r/LocalLLaMA/comments/1kti4xq/which_mac_would_be_better_to_run_a_70_llm_rag/
| false | false |
self
| 1 | null |
Build an AI-Powered Image Search Engine Using Ollama and LangChain
| 0 | 2025-05-23T12:21:49 |
https://youtu.be/S9ugRzGjFtA
|
Flashy-Thought-5472
|
youtu.be
| 1970-01-01T00:00:00 | 0 |
{}
|
1kti6lm
| false |
{'oembed': {'author_name': 'Nariman Codes', 'author_url': 'https://www.youtube.com/@NarimanCodes', 'height': 200, 'html': '<iframe width="356" height="200" src="https://www.youtube.com/embed/S9ugRzGjFtA?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="Build an AI-Powered Image Search Engine Using Ollama and LangChain"></iframe>', 'provider_name': 'YouTube', 'provider_url': 'https://www.youtube.com/', 'thumbnail_height': 360, 'thumbnail_url': 'https://i.ytimg.com/vi/S9ugRzGjFtA/hqdefault.jpg', 'thumbnail_width': 480, 'title': 'Build an AI-Powered Image Search Engine Using Ollama and LangChain', 'type': 'video', 'version': '1.0', 'width': 356}, 'type': 'youtube.com'}
|
t3_1kti6lm
|
/r/LocalLLaMA/comments/1kti6lm/build_an_aipowered_image_search_engine_using/
| false | false | 0 |
{'enabled': False, 'images': [{'id': '1-TbC7xgICLdfvDtCoZXXwzT0BxWOljUGaLj15PAyT8', 'resolutions': [{'height': 81, 'url': 'https://external-preview.redd.it/jZZ-3zedZFX9Wnt3EOLs3mXslHDcJPVGe-EfHw_CU0E.jpg?width=108&crop=smart&auto=webp&s=99edadbd965a187abcd58a35c769f6217c261142', 'width': 108}, {'height': 162, 'url': 'https://external-preview.redd.it/jZZ-3zedZFX9Wnt3EOLs3mXslHDcJPVGe-EfHw_CU0E.jpg?width=216&crop=smart&auto=webp&s=1b2b5f5ad00a68d182ffb39650f77deb09e36ec0', 'width': 216}, {'height': 240, 'url': 'https://external-preview.redd.it/jZZ-3zedZFX9Wnt3EOLs3mXslHDcJPVGe-EfHw_CU0E.jpg?width=320&crop=smart&auto=webp&s=eab22b233cbeca6bb253833740ce334d0dd1d333', 'width': 320}], 'source': {'height': 360, 'url': 'https://external-preview.redd.it/jZZ-3zedZFX9Wnt3EOLs3mXslHDcJPVGe-EfHw_CU0E.jpg?auto=webp&s=f3b048e159bd46be7bfe0022682745c3728de399', 'width': 480}, 'variants': {}}]}
|
||
What's the most accurate way to convert arxiv papers to markdown?
| 15 |
Looking for the best method/library to convert arxiv papers to markdown. It could be from PDF conversion or using HTML like [ar5iv.labs.arxiv.org](http://ar5iv.labs.arxiv.org) .
I tried [marker](https://github.com/VikParuchuri/marker), however, often it does not seem to handle well page breaks and footnotes. Also the section levels are often incorrect.
| 2025-05-23T12:26:19 |
https://www.reddit.com/r/LocalLLaMA/comments/1kti9u1/whats_the_most_accurate_way_to_convert_arxiv/
|
nextlevelhollerith
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1kti9u1
| false | null |
t3_1kti9u1
|
/r/LocalLLaMA/comments/1kti9u1/whats_the_most_accurate_way_to_convert_arxiv/
| false | false |
self
| 15 |
{'enabled': False, 'images': [{'id': 'QWKDmv4fL5OQcwCo2pK8KRJ6iuXnm2FWKpOIegLzclo', 'resolutions': [{'height': 108, 'url': 'https://external-preview.redd.it/gQF_YfxecQZbgUW6xB-K2BEqPfKpf06XWu6CbPfqmLA.jpg?width=108&crop=smart&auto=webp&s=682e1eea70e9a1ca01f0d143b769e9fa5fb2ee1a', 'width': 108}, {'height': 216, 'url': 'https://external-preview.redd.it/gQF_YfxecQZbgUW6xB-K2BEqPfKpf06XWu6CbPfqmLA.jpg?width=216&crop=smart&auto=webp&s=20d2a7bea4dc833c7c4377d3ab951c56ca0461ea', 'width': 216}, {'height': 320, 'url': 'https://external-preview.redd.it/gQF_YfxecQZbgUW6xB-K2BEqPfKpf06XWu6CbPfqmLA.jpg?width=320&crop=smart&auto=webp&s=0744331a42e0902891d59cb350799fce118fc15f', 'width': 320}, {'height': 640, 'url': 'https://external-preview.redd.it/gQF_YfxecQZbgUW6xB-K2BEqPfKpf06XWu6CbPfqmLA.jpg?width=640&crop=smart&auto=webp&s=cbfad47354f46ceeb75ec44764e1e44ea746216a', 'width': 640}, {'height': 960, 'url': 'https://external-preview.redd.it/gQF_YfxecQZbgUW6xB-K2BEqPfKpf06XWu6CbPfqmLA.jpg?width=960&crop=smart&auto=webp&s=ae4791e4e75c3806394e22a690e2220f24107289', 'width': 960}], 'source': {'height': 1000, 'url': 'https://external-preview.redd.it/gQF_YfxecQZbgUW6xB-K2BEqPfKpf06XWu6CbPfqmLA.jpg?auto=webp&s=862a7b1fa2e6880702bf2866570be77e3c351476', 'width': 1000}, 'variants': {}}]}
|
A Demonstration of Cache-Augmented Generation (CAG) and its Performance Comparison to RAG
| 44 |
This project demonstrates how to implement Cache-Augmented Generation (CAG) in an LLM and shows its performance gains compared to RAG.
Project Link: [https://github.com/ronantakizawa/cacheaugmentedgeneration](https://github.com/ronantakizawa/cacheaugmentedgeneration)
CAG preloads document content into an LLM’s context as a precomputed key-value (KV) cache.
This caching eliminates the need for real-time retrieval during inference, reducing token usage by up to 76% while maintaining answer quality.
CAG is particularly effective for constrained knowledge bases like internal documentation, FAQs, and customer support systems, where all relevant information can fit within the model's extended context window.
| 2025-05-23T12:33:08 |
Ok_Employee_6418
|
i.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktiere
| false | null |
t3_1ktiere
|
/r/LocalLLaMA/comments/1ktiere/a_demonstration_of_cacheaugmented_generation_cag/
| false | false | 44 |
{'enabled': True, 'images': [{'id': 'AIXPAwyQkSwFhmFS7dpTX429pVBEiHq8hh2NALQCZgY', 'resolutions': [{'height': 76, 'url': 'https://preview.redd.it/bn39fvozzi2f1.png?width=108&crop=smart&auto=webp&s=b1e021449ba2bfb827b8aacbb98e59d396b4490e', 'width': 108}, {'height': 153, 'url': 'https://preview.redd.it/bn39fvozzi2f1.png?width=216&crop=smart&auto=webp&s=67737fb7d84ed5d5ce78381170a57365f4d2bf92', 'width': 216}, {'height': 226, 'url': 'https://preview.redd.it/bn39fvozzi2f1.png?width=320&crop=smart&auto=webp&s=90755a5a369715bcd2414437886f510e03ef377b', 'width': 320}, {'height': 453, 'url': 'https://preview.redd.it/bn39fvozzi2f1.png?width=640&crop=smart&auto=webp&s=9702ce1baab0703350e9800e0619c24d489b70eb', 'width': 640}], 'source': {'height': 496, 'url': 'https://preview.redd.it/bn39fvozzi2f1.png?auto=webp&s=b5a7dee7eca8ae82a91baaeaed1a05b637df654d', 'width': 700}, 'variants': {}}]}
|
||
All I wanted is a simple FREE chat app
| 1 |
[removed]
| 2025-05-23T12:38:30 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktiik1/all_i_wanted_is_a_simple_free_chat_app/
|
COBECT
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktiik1
| false | null |
t3_1ktiik1
|
/r/LocalLLaMA/comments/1ktiik1/all_i_wanted_is_a_simple_free_chat_app/
| false | false |
self
| 1 |
{'enabled': False, 'images': [{'id': '-ctwWkN6rHGc2V6GtsAmk-HLdFHSpEj4U0gSuMMDRmw', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/OsoAgJqfaL_UgiiQdsx-291iQtC4URluQgtyHkpiGeE.jpg?width=108&crop=smart&auto=webp&s=f35549a0260f3dffaecfe008535d98df9d849414', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/OsoAgJqfaL_UgiiQdsx-291iQtC4URluQgtyHkpiGeE.jpg?width=216&crop=smart&auto=webp&s=114731a6bcfdad6fd0883c8b2a70f73220d22b2f', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/OsoAgJqfaL_UgiiQdsx-291iQtC4URluQgtyHkpiGeE.jpg?width=320&crop=smart&auto=webp&s=c8fb76daec27d80fcc15f0cefd8e91282936cd19', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/OsoAgJqfaL_UgiiQdsx-291iQtC4URluQgtyHkpiGeE.jpg?width=640&crop=smart&auto=webp&s=0383505fe666d8be50a1d3fe9573db90bb6261d1', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/OsoAgJqfaL_UgiiQdsx-291iQtC4URluQgtyHkpiGeE.jpg?width=960&crop=smart&auto=webp&s=82f9d2f0b51b75324d44083284dd3ceee6c693a6', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/OsoAgJqfaL_UgiiQdsx-291iQtC4URluQgtyHkpiGeE.jpg?width=1080&crop=smart&auto=webp&s=bdede698022f902c3057b82e81f2c0712657c58f', 'width': 1080}], 'source': {'height': 1350, 'url': 'https://external-preview.redd.it/OsoAgJqfaL_UgiiQdsx-291iQtC4URluQgtyHkpiGeE.jpg?auto=webp&s=8d7458ab24160c3de9b7569fcb5ec2c622537d11', 'width': 2400}, 'variants': {}}]}
|
I accidentally too many P100
| 417 |
Hi, I had quite positive results with a P100 last summer, so when R1 came out, I decided to try if I could put 16 of them in a single pc... and I could.
Not the fastest think in the universe, and I am not getting awesome PCIE speed (2@4x). But it works, is still cheaper than a 5090, and I hope I can run stuff with large contexts.
I hoped to run llama4 with large context sizes, and scout runs almost ok, but llama4 as a model is abysmal. I tried to run Qwen3-235B-A22B, but the performance with llama.cpp is pretty terrible, and I haven't been able to get it working with the vllm-pascal (ghcr.io/sasha0552/vllm:latest).
If you have any pointers on getting Qwen3-235B to run with any sort of parallelism, or want me to benchmark any model, just say so!
The MB is a 2014 intel S2600CW with dual 8-core xeons, so CPU performance is rather low. I also tried to use MB with an EPYC, but it doesn't manage to allocate the resources to all PCIe devices.
| 2025-05-23T12:48:51 |
https://www.reddit.com/gallery/1ktiq99
|
TooManyPascals
|
reddit.com
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktiq99
| false | null |
t3_1ktiq99
|
/r/LocalLLaMA/comments/1ktiq99/i_accidentally_too_many_p100/
| false | false | 417 | null |
|
nanoVLM: The simplest repository to train your VLM in pure PyTorch
| 27 | 2025-05-23T12:54:55 |
https://huggingface.co/blog/nanovlm
|
ab2377
|
huggingface.co
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktiusw
| false | null |
t3_1ktiusw
|
/r/LocalLLaMA/comments/1ktiusw/nanovlm_the_simplest_repository_to_train_your_vlm/
| false | false | 27 |
{'enabled': False, 'images': [{'id': 'YLeFYXJmc-iscz_0rCXh7lML-AboTi25K0CW6HUv1nE', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/k3XI6YWGCxh9L4PoRExljDZTmAkbUgwnwQi71BtdC9A.jpg?width=108&crop=smart&auto=webp&s=f117134956e07deb8bb1ac1a9b826a6b4681c0ad', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/k3XI6YWGCxh9L4PoRExljDZTmAkbUgwnwQi71BtdC9A.jpg?width=216&crop=smart&auto=webp&s=7b304e6c51d9e40e41e0f74622efb74cf05b8465', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/k3XI6YWGCxh9L4PoRExljDZTmAkbUgwnwQi71BtdC9A.jpg?width=320&crop=smart&auto=webp&s=1283f717572a5bbba38faa7dfa3b4ec2f60d3570', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/k3XI6YWGCxh9L4PoRExljDZTmAkbUgwnwQi71BtdC9A.jpg?width=640&crop=smart&auto=webp&s=06978f4f95414bba1cfe00e253ce645b2a32d135', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/k3XI6YWGCxh9L4PoRExljDZTmAkbUgwnwQi71BtdC9A.jpg?width=960&crop=smart&auto=webp&s=8a8114d523a32a3c6cdd891c468d120de1cf44c0', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/k3XI6YWGCxh9L4PoRExljDZTmAkbUgwnwQi71BtdC9A.jpg?width=1080&crop=smart&auto=webp&s=e69296c0446d4f6e645dc88840075f40d8a16358', 'width': 1080}], 'source': {'height': 1080, 'url': 'https://external-preview.redd.it/k3XI6YWGCxh9L4PoRExljDZTmAkbUgwnwQi71BtdC9A.jpg?auto=webp&s=659974b2d98e1aedab6bfbb440c497453e55555f', 'width': 1920}, 'variants': {}}]}
|
||
Ollama is running on AMD GPU, despite ROCM not being installed
| 1 |
[removed]
| 2025-05-23T13:24:10 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktjhml/ollama_is_running_on_amd_gpu_despite_rocm_not/
|
Xatraxalian
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktjhml
| false | null |
t3_1ktjhml
|
/r/LocalLLaMA/comments/1ktjhml/ollama_is_running_on_amd_gpu_despite_rocm_not/
| false | false |
self
| 1 |
{'enabled': False, 'images': [{'id': 'q0Dze0o_SCG5-XBdM5y1Qobni-JTLZfbkgXs6Pktjwc', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/9vs91oHqrq4ALJKGEHT7pzTbrDc2nQp7iYho6pcEIfo.jpg?width=108&crop=smart&auto=webp&s=e1162aec77faeaac52274e4ce6a9b488d8554330', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/9vs91oHqrq4ALJKGEHT7pzTbrDc2nQp7iYho6pcEIfo.jpg?width=216&crop=smart&auto=webp&s=a618019b8eae8eebdf3d73bb55fb9b081aae298f', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/9vs91oHqrq4ALJKGEHT7pzTbrDc2nQp7iYho6pcEIfo.jpg?width=320&crop=smart&auto=webp&s=d9f441152cdd4752e3d0edc8dc4867b86e251e35', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/9vs91oHqrq4ALJKGEHT7pzTbrDc2nQp7iYho6pcEIfo.jpg?width=640&crop=smart&auto=webp&s=c74b9e88fc860ac65775a5bf5352882a9a7b1613', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/9vs91oHqrq4ALJKGEHT7pzTbrDc2nQp7iYho6pcEIfo.jpg?width=960&crop=smart&auto=webp&s=cc5dfecd9ef81ada6344b98d2a8ea7435dffbf7a', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/9vs91oHqrq4ALJKGEHT7pzTbrDc2nQp7iYho6pcEIfo.jpg?width=1080&crop=smart&auto=webp&s=a0c2b8566873442329fe33a5e9841b0fdb03e7aa', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/9vs91oHqrq4ALJKGEHT7pzTbrDc2nQp7iYho6pcEIfo.jpg?auto=webp&s=c9570c603f9797311478be028fd43a9aef9f6de7', 'width': 1200}, 'variants': {}}]}
|
What model should I choose?
| 1 |
[removed]
| 2025-05-23T13:26:07 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktjj3l/what_model_should_i_choose/
|
Abject_Personality53
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktjj3l
| false | null |
t3_1ktjj3l
|
/r/LocalLLaMA/comments/1ktjj3l/what_model_should_i_choose/
| false | false |
self
| 1 | null |
What's the current state of art method for using "scratch pads"?
| 3 |
Using scratch pads were very popular back in the olden days of 2023 due to extremely small context lengths. They maxed out at around 8k tokens. But now with agents, we're running into context length issues once again.
I haven't kept up with the research in this area, so what are the current best methods for using scratch pads in agentic settings so the model doesn't lose the thread on what its original goals were and what things it has tried and has yet to try?
| 2025-05-23T13:51:41 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktk3hi/whats_the_current_state_of_art_method_for_using/
|
drooolingidiot
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktk3hi
| false | null |
t3_1ktk3hi
|
/r/LocalLLaMA/comments/1ktk3hi/whats_the_current_state_of_art_method_for_using/
| false | false |
self
| 3 | null |
Opensource LLM for enterprise RAG use case, Qwen3 benchmark validation
| 1 |
[removed]
| 2025-05-23T13:54:20 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktk5q7/opensource_llm_for_enterprise_rag_use_case_qwen3/
|
SK33LA
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktk5q7
| false | null |
t3_1ktk5q7
|
/r/LocalLLaMA/comments/1ktk5q7/opensource_llm_for_enterprise_rag_use_case_qwen3/
| false | false |
self
| 1 | null |
Question for RAG LLMs and Qwen3 benchmark
| 1 |
[removed]
| 2025-05-23T13:57:41 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktk8lh/question_for_rag_llms_and_qwen3_benchmark/
|
SK33LA
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktk8lh
| false | null |
t3_1ktk8lh
|
/r/LocalLLaMA/comments/1ktk8lh/question_for_rag_llms_and_qwen3_benchmark/
| false | false |
self
| 1 | null |
Unmute by Kyutai: Make LLMs listen and speak
| 1 |
[removed]
| 2025-05-23T14:06:54 |
https://kyutai.org/2025/05/22/unmute.html
|
rerri
|
kyutai.org
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktkgl1
| false | null |
t3_1ktkgl1
|
/r/LocalLLaMA/comments/1ktkgl1/unmute_by_kyutai_make_llms_listen_and_speak/
| false | false |
default
| 1 | null |
It never ends with these people, no matter how far you go
| 0 | 2025-05-23T14:08:11 |
baobabKoodaa
|
i.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktkhof
| false | null |
t3_1ktkhof
|
/r/LocalLLaMA/comments/1ktkhof/it_never_ends_with_these_people_no_matter_how_far/
| false | false | 0 |
{'enabled': True, 'images': [{'id': 'Fs6wKucI7XQXvgSK7-pb0m7HMZvAcyIEwKkAqmqfGQ0', 'resolutions': [{'height': 112, 'url': 'https://preview.redd.it/3z82k151hj2f1.png?width=108&crop=smart&auto=webp&s=73022bcd93e86066cf43a993fb8cbdc002a73589', 'width': 108}, {'height': 224, 'url': 'https://preview.redd.it/3z82k151hj2f1.png?width=216&crop=smart&auto=webp&s=3d46eaf1b78736837d7d13c6b1b9f2589ba789c6', 'width': 216}, {'height': 332, 'url': 'https://preview.redd.it/3z82k151hj2f1.png?width=320&crop=smart&auto=webp&s=1e976e41cd1e8dfe58c79b6ea51a28aa466488b8', 'width': 320}, {'height': 664, 'url': 'https://preview.redd.it/3z82k151hj2f1.png?width=640&crop=smart&auto=webp&s=11545b5a977a469161bba960827be77d7134447e', 'width': 640}], 'source': {'height': 790, 'url': 'https://preview.redd.it/3z82k151hj2f1.png?auto=webp&s=d4890fdd2341232c174c8e64157ac162e33228fd', 'width': 761}, 'variants': {}}]}
|
|||
Claude 4 (Sonnet) isn't great for document understanding tasks: some surprising results
| 112 |
Finished benchmarking Claude 4 (Sonnet) across a range of document understanding tasks, and the results are… not that good. It's currently **ranked 7th overall** on the leaderboard.
Key takeaways:
* Weak performance in OCR – Claude 4 lags behind even smaller models like GPT-4.1-nano and InternVL3-38B-Instruct.
* Rotation sensitivity – We tested OCR robustness with slightly rotated images (\[-5°, +5°\]). Most large models had a 2–3% drop in accuracy. Claude 4 dropped 9%.
* Poor on handwritten documents – Scored only 51.64%, while Gemini 2.0 Flash got 71.24%. It also struggled with handwritten datasets in other tasks like key information extraction.
* Chart VQA and visual tasks – Performed decently but still behind Gemini, Claude 3.7, and GPT-4.5/o4-mini.
* Long document understanding – Claude 3.7 Sonnet (reasoning:low) ranked 1st. Claude 4 Sonnet ranked 13th.
* **One bright spot: table extraction** – Claude 4 Sonnet is currently ranked 1st, narrowly ahead of Claude 3.7 Sonnet.
https://preview.redd.it/72zkmcyogj2f1.png?width=2448&format=png&auto=webp&s=cc8fb9e86ca0bcfe129e25dab934d06818f7d638
Leaderboard: [https://idp-leaderboard.org/](https://idp-leaderboard.org/)
Codebase: [https://github.com/NanoNets/docext](https://github.com/NanoNets/docext)
How has everyone’s experience with the models been so far?
| 2025-05-23T14:08:12 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktkhp8/claude_4_sonnet_isnt_great_for_document/
|
SouvikMandal
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktkhp8
| false | null |
t3_1ktkhp8
|
/r/LocalLLaMA/comments/1ktkhp8/claude_4_sonnet_isnt_great_for_document/
| false | false | 112 | null |
|
Unmute by Kyutai: Make LLMs listen and speak
| 188 |
Seems nicely polished and apparently works with any LLM. Open-source in the coming weeks.
Demo uses Gemma 3 12B as base LLM (demo link in the blog post, reddit seems to auto-delete my post if I include it here).
If any Kyutai dev happens to lurk here, would love to hear about the memory requirements of the TTS & STT models.
| 2025-05-23T14:12:46 |
https://kyutai.org/2025/05/22/unmute.html
|
rerri
|
kyutai.org
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktklo5
| false | null |
t3_1ktklo5
|
/r/LocalLLaMA/comments/1ktklo5/unmute_by_kyutai_make_llms_listen_and_speak/
| false | false |
default
| 188 | null |
What model should I choose?
| 1 |
[removed]
| 2025-05-23T14:14:02 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktkmqh/what_model_should_i_choose/
|
Abject_Personality53
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktkmqh
| false | null |
t3_1ktkmqh
|
/r/LocalLLaMA/comments/1ktkmqh/what_model_should_i_choose/
| false | false |
self
| 1 | null |
Strategies for aligning embedded text in PDF into a logical order
| 2 |
So I have some PDFs which have text information embedded and these are essentially bank statements with items in rows with amounts.
However, if you try to select them in a PDF viewer, the text is everywhere as the embedded text is not in any sane order. This is massively frustrating since the accurate embedded text is there but not in a usable state.
Has anyone tackled this problem and figured out a good way to align/re-order text without just re-OCR'ing it (which is subject to OCR errors)?
| 2025-05-23T14:46:56 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktleg0/strategies_for_aligning_embedded_text_in_pdf_into/
|
DeltaSqueezer
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktleg0
| false | null |
t3_1ktleg0
|
/r/LocalLLaMA/comments/1ktleg0/strategies_for_aligning_embedded_text_in_pdf_into/
| false | false |
self
| 2 | null |
96GB VRAM! What should run first?
| 1,462 |
I had to make a fake company domain name to order this from a supplier. They wouldn’t even give me a quote with my Gmail address. I got the card though!
| 2025-05-23T15:10:20 |
Mother_Occasion_8076
|
i.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktlz3w
| false | null |
t3_1ktlz3w
|
/r/LocalLLaMA/comments/1ktlz3w/96gb_vram_what_should_run_first/
| false | false | 1,462 |
{'enabled': True, 'images': [{'id': 'uU6dM4WijM_cYbJ_ExiJXAu9rQhwKqr0Nz3u14SWZ3E', 'resolutions': [{'height': 81, 'url': 'https://preview.redd.it/co0zhh06sj2f1.jpeg?width=108&crop=smart&auto=webp&s=a35164fe77c202ec5b589dfe668feb1e80c255c0', 'width': 108}, {'height': 162, 'url': 'https://preview.redd.it/co0zhh06sj2f1.jpeg?width=216&crop=smart&auto=webp&s=4bf4f14af20ed83f34bdad4529d0dd8d0f7bd723', 'width': 216}, {'height': 240, 'url': 'https://preview.redd.it/co0zhh06sj2f1.jpeg?width=320&crop=smart&auto=webp&s=751d144e88751fb8a35d144ba9c555f2c5f7ad38', 'width': 320}, {'height': 480, 'url': 'https://preview.redd.it/co0zhh06sj2f1.jpeg?width=640&crop=smart&auto=webp&s=64b43f0124c5d5b397b2efd848e6e83c1dcfcfdc', 'width': 640}, {'height': 720, 'url': 'https://preview.redd.it/co0zhh06sj2f1.jpeg?width=960&crop=smart&auto=webp&s=c3fef92ceabd6da8ee3e6f0149b625189e1bd552', 'width': 960}, {'height': 810, 'url': 'https://preview.redd.it/co0zhh06sj2f1.jpeg?width=1080&crop=smart&auto=webp&s=5fe686cf45cf6357bc7a300e794f1317c06a1cb5', 'width': 1080}], 'source': {'height': 3024, 'url': 'https://preview.redd.it/co0zhh06sj2f1.jpeg?auto=webp&s=299743d41bdf692b635b669a0b8bad54388da446', 'width': 4032}, 'variants': {}}]}
|
||
AI becoming too sycophantic? Noticed Gemini 2.5 praising me instead of solving the issue
| 98 |
Hello there, I get the feeling that the trend of making AI more inclined towards flattery and overly focused on a user's feelings is somehow degrading its ability to actually solve problems. Is it just me? For instance, I've recently noticed that Gemini 2.5, instead of giving a direct solution, will spend time praising me, saying I'm using the right programming paradigms, blah blah blah, and that my code should generally work. In the end, it was no help at all. Qwen2 32B, on the other hand, just straightforwardly pointed out my error.
| 2025-05-23T15:11:54 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktm0hd/ai_becoming_too_sycophantic_noticed_gemini_25/
|
Rrraptr
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktm0hd
| false | null |
t3_1ktm0hd
|
/r/LocalLLaMA/comments/1ktm0hd/ai_becoming_too_sycophantic_noticed_gemini_25/
| false | false |
self
| 98 | null |
Sarvam-M a 24B open-weights hybrid reasoning model
| 6 |
Model Link: [https://huggingface.co/sarvamai/sarvam-m](https://huggingface.co/sarvamai/sarvam-m)
Model Info: It's a 2 staged post trained version of Mistral 24B on SFT and GRPO.
It's a hybrid reasoning model which means that both reasoning and non-reasoning models are fitted in same model. You can choose when to reason and when not.
If you wanna try you can either run it locally or from Sarvam's platform.
[https://dashboard.sarvam.ai/playground](https://dashboard.sarvam.ai/playground)
Also, they released detailed blog post on post training: [https://www.sarvam.ai/blogs/sarvam-m](https://www.sarvam.ai/blogs/sarvam-m)
| 2025-05-23T15:13:11 |
RealKingNish
|
i.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktm1n7
| false | null |
t3_1ktm1n7
|
/r/LocalLLaMA/comments/1ktm1n7/sarvamm_a_24b_openweights_hybrid_reasoning_model/
| false | false | 6 |
{'enabled': True, 'images': [{'id': 'DmMsBRPNbi849LghsoO44o2QAnMJPTmgh7bjxAlSNrE', 'resolutions': [{'height': 108, 'url': 'https://preview.redd.it/8gk7kugnsj2f1.png?width=108&crop=smart&auto=webp&s=e758ae8dd0759d6b6eaaa31b4cdaf08d467f7ba4', 'width': 108}, {'height': 216, 'url': 'https://preview.redd.it/8gk7kugnsj2f1.png?width=216&crop=smart&auto=webp&s=93fb11d3e2b82c7eb4599407eb0777f6620e0cd6', 'width': 216}, {'height': 320, 'url': 'https://preview.redd.it/8gk7kugnsj2f1.png?width=320&crop=smart&auto=webp&s=5411444b8f48ecf53b0551150b295cbcb5cf4892', 'width': 320}, {'height': 640, 'url': 'https://preview.redd.it/8gk7kugnsj2f1.png?width=640&crop=smart&auto=webp&s=cfc3a087396e0a4a8f0a79dc5b3427bd30d54414', 'width': 640}, {'height': 960, 'url': 'https://preview.redd.it/8gk7kugnsj2f1.png?width=960&crop=smart&auto=webp&s=0d0194072e863feff29d142f4a0dca4c0826bf48', 'width': 960}, {'height': 1080, 'url': 'https://preview.redd.it/8gk7kugnsj2f1.png?width=1080&crop=smart&auto=webp&s=f261562d5ba0559c920735d5d8f037c1ccadaadf', 'width': 1080}], 'source': {'height': 1080, 'url': 'https://preview.redd.it/8gk7kugnsj2f1.png?auto=webp&s=ceffb46802f3179eefa0c5e5658919169d4cb5dd', 'width': 1080}, 'variants': {}}]}
|
||
What model should I choose?
| 5 |
I study in medical field and I cannot stomach hours of search in books anymore. So I would like to run AI that will take books(they will be both in Russian and English) as context and spew answer to the questions while also providing reference, so that I can check, memorise and take notes. I don't mind the waiting of 30-60 minutes per answer, but I need maximum accuracy.
I have laptop(yeah, regular PC is not suitable for me) with
i9-13900hx
4080 laptop(12gb)
16gb ddr5 so-dimm
If there's a need for more ram, I'm ready to buy Crucial DDR5 sodimm 2×64gb kit. Also, I'm absolute beginner, so I'm not sure if it's even possible
| 2025-05-23T15:13:45 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktm248/what_model_should_i_choose/
|
Abject_Personality53
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktm248
| false | null |
t3_1ktm248
|
/r/LocalLLaMA/comments/1ktm248/what_model_should_i_choose/
| false | false |
self
| 5 | null |
Spatial Reasoning is Hot 🔥🔥🔥🔥🔥🔥
| 21 |
Notice the recent uptick in google search interest around "spatial reasoning."
And now we have a fantastic new benchmark to better measure these capabilities.
**SpatialScore:** [https://haoningwu3639.github.io/SpatialScore/](https://haoningwu3639.github.io/SpatialScore/)
The **SpatialScore** benchmarks offer a comprehensive assessment covering key spatial reasoning capabilities like:
obj counting
2D localization
3D distance estimation
This benchmark can help drive progress in adapting VLMs for embodied AI use cases in robotics, where perception and planning hinge on strong spatial understanding.
| 2025-05-23T15:26:50 |
https://www.reddit.com/gallery/1ktmdpo
|
remyxai
|
reddit.com
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktmdpo
| false | null |
t3_1ktmdpo
|
/r/LocalLLaMA/comments/1ktmdpo/spatial_reasoning_is_hot/
| false | false | 21 | null |
|
LLMI system I (not my money) got for the group
| 1 |
[removed]
| 2025-05-23T16:50:04 |
SandboChang
|
i.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktof5c
| false | null |
t3_1ktof5c
|
/r/LocalLLaMA/comments/1ktof5c/llmi_system_i_not_my_money_got_for_the_group/
| false | false | 1 |
{'enabled': True, 'images': [{'id': 'zFAVAcLpeVKajxvMYo0NM4QH2u1z5gaf3i4_RnKaJcE', 'resolutions': [{'height': 81, 'url': 'https://preview.redd.it/xlu1hsfj3k2f1.jpeg?width=108&crop=smart&auto=webp&s=7cd906e4c120d560993394c54387518fba3c89ee', 'width': 108}, {'height': 162, 'url': 'https://preview.redd.it/xlu1hsfj3k2f1.jpeg?width=216&crop=smart&auto=webp&s=5b899340d15c0d7e01f2d50ac456b6e5b2646679', 'width': 216}, {'height': 240, 'url': 'https://preview.redd.it/xlu1hsfj3k2f1.jpeg?width=320&crop=smart&auto=webp&s=392fc0ba3c5381e4f52a2edf6c165faca4188f1c', 'width': 320}, {'height': 480, 'url': 'https://preview.redd.it/xlu1hsfj3k2f1.jpeg?width=640&crop=smart&auto=webp&s=9a51c579d96fde3dec00be4a4801bc5ea965bd15', 'width': 640}, {'height': 720, 'url': 'https://preview.redd.it/xlu1hsfj3k2f1.jpeg?width=960&crop=smart&auto=webp&s=e4ae5f44272fbdd3c2b637f4d5331cac48a4269a', 'width': 960}, {'height': 810, 'url': 'https://preview.redd.it/xlu1hsfj3k2f1.jpeg?width=1080&crop=smart&auto=webp&s=75799951636c2bcc7b8c58187a01a705848e51c0', 'width': 1080}], 'source': {'height': 3024, 'url': 'https://preview.redd.it/xlu1hsfj3k2f1.jpeg?auto=webp&s=4b99e68f9a5eb42dfa6e57f601d0f4a44fd05bfd', 'width': 4032}, 'variants': {}}]}
|
||
LLMI system I (not my money) got for our group
| 180 | 2025-05-23T16:52:23 |
SandboChang
|
i.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktoh78
| false | null |
t3_1ktoh78
|
/r/LocalLLaMA/comments/1ktoh78/llmi_system_i_not_my_money_got_for_our_group/
| false | false | 180 |
{'enabled': True, 'images': [{'id': 'Y9oM7DtsJUSL1S_CXDfvsrN56xHFNxKI0_W5nDUOHOY', 'resolutions': [{'height': 81, 'url': 'https://preview.redd.it/lgjexuw8ak2f1.jpeg?width=108&crop=smart&auto=webp&s=4e7502705e0b589d6e33a689210490d1546b1048', 'width': 108}, {'height': 162, 'url': 'https://preview.redd.it/lgjexuw8ak2f1.jpeg?width=216&crop=smart&auto=webp&s=1da25e2b05b64af8ff844470f7665fbecb01051b', 'width': 216}, {'height': 240, 'url': 'https://preview.redd.it/lgjexuw8ak2f1.jpeg?width=320&crop=smart&auto=webp&s=063c3694057f834a06bbb62a8fe1a22ab5851eb2', 'width': 320}, {'height': 480, 'url': 'https://preview.redd.it/lgjexuw8ak2f1.jpeg?width=640&crop=smart&auto=webp&s=3260ccc53dd2f7cca5692637366920fd7a9928ec', 'width': 640}, {'height': 720, 'url': 'https://preview.redd.it/lgjexuw8ak2f1.jpeg?width=960&crop=smart&auto=webp&s=c79d5cb0d5dc40af105d2eb0820185d0243f5680', 'width': 960}, {'height': 810, 'url': 'https://preview.redd.it/lgjexuw8ak2f1.jpeg?width=1080&crop=smart&auto=webp&s=4c04ec7818ec0987d6202de1e96705fce6a63853', 'width': 1080}], 'source': {'height': 3024, 'url': 'https://preview.redd.it/lgjexuw8ak2f1.jpeg?auto=webp&s=d75cbc80f95ae45e7039778d8e4b13677a75e55b', 'width': 4032}, 'variants': {}}]}
|
|||
So what are some cool projects you guys are running on you local llms?
| 58 |
Trying to find good ideas to implement on my setup, or maybe get some inspiration to do something on my own
| 2025-05-23T16:55:33 |
https://www.reddit.com/r/LocalLLaMA/comments/1ktojxe/so_what_are_some_cool_projects_you_guys_are/
|
itzikhan
|
self.LocalLLaMA
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktojxe
| false | null |
t3_1ktojxe
|
/r/LocalLLaMA/comments/1ktojxe/so_what_are_some_cool_projects_you_guys_are/
| false | false |
self
| 58 | null |
SLM RAG Arena - Compare and Find The Best Sub-5B Models for RAG
| 1 |
[removed]
| 2025-05-23T16:59:48 |
unseenmarscai
|
i.redd.it
| 1970-01-01T00:00:00 | 0 |
{}
|
1ktonl6
| false | null |
t3_1ktonl6
|
/r/LocalLLaMA/comments/1ktonl6/slm_rag_arena_compare_and_find_the_best_sub5b/
| false | false | 1 |
{'enabled': True, 'images': [{'id': '55U7Y6g4TiBPTPu9wNpkYhC4p_9LUOfeCIvByvx4dvk', 'resolutions': [{'height': 60, 'url': 'https://preview.redd.it/ikvqvvaaak2f1.png?width=108&crop=smart&auto=webp&s=4c200607742f9f8a42ad682ce0c5210eeacd57a4', 'width': 108}, {'height': 121, 'url': 'https://preview.redd.it/ikvqvvaaak2f1.png?width=216&crop=smart&auto=webp&s=2e806308916e28a3573ed795535871f002c87f52', 'width': 216}, {'height': 180, 'url': 'https://preview.redd.it/ikvqvvaaak2f1.png?width=320&crop=smart&auto=webp&s=fdaa57df939fa01d697269d2d442d5d200b72ac8', 'width': 320}, {'height': 360, 'url': 'https://preview.redd.it/ikvqvvaaak2f1.png?width=640&crop=smart&auto=webp&s=ede267f01a193bdc2af9f8a3ef2704bbb6d31732', 'width': 640}, {'height': 540, 'url': 'https://preview.redd.it/ikvqvvaaak2f1.png?width=960&crop=smart&auto=webp&s=7d29ca9bf5ec99e00a68815a7929fb9cc56bd83b', 'width': 960}, {'height': 607, 'url': 'https://preview.redd.it/ikvqvvaaak2f1.png?width=1080&crop=smart&auto=webp&s=ca60ce139c1f69dbc613606e6b6ca92a84a79a5e', 'width': 1080}], 'source': {'height': 1080, 'url': 'https://preview.redd.it/ikvqvvaaak2f1.png?auto=webp&s=f0e08d8a8bc15a87b7d2dcb33911f8f89c28387f', 'width': 1920}, 'variants': {}}]}
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.