name string | body string | score int64 | controversiality int64 | created timestamp[us] | author string | collapsed bool | edited timestamp[us] | gilded int64 | id string | locked bool | permalink string | stickied bool | ups int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
t1_o8a14lt | Got way too excited and tried it with `Qwen3.5-4B-Q8_0.gguf` but it crashed every time I tried to load it into chat.
On `v0.8.8`. | 1 | 0 | 2026-03-02T19:08:11 | ANONYMOUSEJR | false | null | 0 | o8a14lt | false | /r/LocalLLaMA/comments/1riv3wv/qwen_35_2b_on_android/o8a14lt/ | false | 1 |
t1_o8a14kd | [deleted] | 1 | 0 | 2026-03-02T19:08:10 | [deleted] | true | null | 0 | o8a14kd | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o8a14kd/ | false | 1 |
t1_o8a11wt | Yes, I start with 22-23.5 on VRAM, then the rest gets loaded onto RAM.
I try to aim for a max of 60-65% of total memory budget (152GB combined) when I pick models... so no more than 99-ish GB GGUF file size when I pick models. Bartowski and Unsloth IQ2's work fine (GLM, Minimax, etc)
Sure, it's slow 2-5 tk/s. But I'm a very patient person.
Privacy > Super Fast Token Speeds
For smaller models I can get 15-20+ tk/s if I'm in a hurry | 1 | 0 | 2026-03-02T19:07:49 | misterflyer | false | null | 0 | o8a11wt | false | /r/LocalLLaMA/comments/1ria14c/dario_amodei_on_open_source_thoughts/o8a11wt/ | false | 1 |
t1_o8a0z82 | Nice Post!
I can share a very simple example and visuals to understand Temperature, Top P and Top K. The video is for AWS Bedrock but Temperature, Top P and Top K concepts are the same: [https://youtu.be/dHmf1Xojr5w](https://youtu.be/dHmf1Xojr5w) | 1 | 0 | 2026-03-02T19:07:28 | Significant-Pitch-22 | false | null | 0 | o8a0z82 | false | /r/LocalLLaMA/comments/1pj6t0u/i_want_to_help_people_understand_what_the_topk/o8a0z82/ | false | 1 |
t1_o8a0yus | I just tried Qwen, and yes, it's very good. glm-ocr is definitely also capable of it though and is tiny. Maybe give it a better chance? They have their SDK also so it is a bit like Paddle. I am developing an app where I need good OCR and I was very happy yo see a model like glm-ocr. btw their online service is also amazing: [https://ocr.z.ai/](https://ocr.z.ai/) | 1 | 0 | 2026-03-02T19:07:25 | danihend | false | null | 0 | o8a0yus | false | /r/LocalLLaMA/comments/1rivzcl/qwen_35_2b_is_an_ocr_beast/o8a0yus/ | false | 1 |
t1_o8a0vyz | Tried with Pi Coding Agent? With local models we have to be much more conserative with token usage, and the tooling usage is much better implemented in Pi so that it works alot better with local models. I highly suggest everyone to try it out! | 1 | 0 | 2026-03-02T19:07:02 | Freaker79 | false | null | 0 | o8a0vyz | false | /r/LocalLLaMA/comments/1riwy9w/is_qwen359b_enough_for_agentic_coding/o8a0vyz/ | false | 1 |
t1_o8a0rrh | If you were okay with ollama (which contains an outdated/broken llama.cpp inside), you'll be happy with "bare" llama.cpp. It works well with qwen3.5. | 1 | 0 | 2026-03-02T19:06:28 | 666666thats6sixes | false | null | 0 | o8a0rrh | false | /r/LocalLLaMA/comments/1riz7dv/unslothqwen359bggufq8_0_failing_on_ollama/o8a0rrh/ | false | 1 |
t1_o8a0rkt | not for qwen, since it's already included | 1 | 0 | 2026-03-02T19:06:26 | Negative-Web8619 | false | null | 0 | o8a0rkt | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o8a0rkt/ | false | 1 |
t1_o8a0qxt | issue solved once updating lm studio to latest and mlx to beta | 1 | 0 | 2026-03-02T19:06:21 | BitXorBit | false | null | 0 | o8a0qxt | false | /r/LocalLLaMA/comments/1rfacu3/qwen35_122b397b_extremely_slow_json_processing/o8a0qxt/ | false | 1 |
t1_o8a0jux | Why so? What makes those better than cline? | 1 | 0 | 2026-03-02T19:05:25 | Kawaiiwaffledesu | false | null | 0 | o8a0jux | false | /r/LocalLLaMA/comments/1rgtxry/is_qwen35_a_coding_game_changer_for_anyone_else/o8a0jux/ | false | 1 |
t1_o8a0jc9 | Glad you did — this kind of connected writeup is rare. Isolated reports never build momentum; this one should.a | 1 | 0 | 2026-03-02T19:05:21 | theagentledger | false | null | 0 | o8a0jc9 | false | /r/LocalLLaMA/comments/1riwhcf/psa_lm_studios_parser_silently_breaks_qwen35_tool/o8a0jc9/ | false | 1 |
t1_o8a0fje | Thanks!
Training framework: HuggingFace Transformers + TRL (SFTTrainer) throughout. Adafactor optimizer for both CPT and SFT. On a 24GB card with a 3B full fine-tune, Adam's optimizer states alone would eat \~12GB, Adafactor gets that down to a few hundred MB which made the difference.
CPT context length: 1792 tokens (reduced from 2048 to give the backward pass some headroom on the RTX 3090.. full fine-tune gradients are large).
For instruct post-training: planning to stay at 1792 for the active loop SFT iterations, then potentially push to 2048 or higher for the final full SFT pass depending on what the patristic Q&A pairs actually need. Most theological Q&A fits comfortably in 1792 but some of the longer homily passages might benefit from more context.
| 1 | 0 | 2026-03-02T19:04:51 | Financial-Fun-8930 | false | null | 0 | o8a0fje | false | /r/LocalLLaMA/comments/1ribjum/i_trained_a_3b_patristic_theology_llm_on_a_single/o8a0fje/ | false | 1 |
t1_o8a0f10 | 2023 is the beginning of the end: https://old.reddit.com/r/ChatGPT/comments/15nvb6y/as_an_ai_language_model_in_research_papers/
2026 is the end: you can see the vibe-written abstracts with vibe-generated plots in this very sub | 1 | 0 | 2026-03-02T19:04:47 | MelodicRecognition7 | false | null | 0 | o8a0f10 | false | /r/LocalLLaMA/comments/1rj0mxt/why_are_people_so_quick_to_say_closed_frontiers/o8a0f10/ | false | 1 |
t1_o8a0e3e | With your RTX 5060 Ti 16GB, you have plenty of VRAM for those models! For RAG and coding, I'd recommend: 1) Qwen2.5-Coder 14B at Q4 - excellent for code understanding. 2) If you want larger models, try Qwen3 32B at Q4\_K\_M - runs great on 16GB. 3) For even better coding performance, DeepSeek Coder 33B at IQ2.5. Also make sure you're using GPU acceleration in LMStudio and have the right context size set - too large context can slow things down significantly. | 1 | 0 | 2026-03-02T19:04:40 | Pure-Fruit2654 | false | null | 0 | o8a0e3e | false | /r/LocalLLaMA/comments/1rj0dyn/best_compatible_suitable_localllm_model_suggestion/o8a0e3e/ | false | 1 |
t1_o8a0dpt | I was referring to GPT-OSS; personally, I think those models were a failure. First, they delayed the release to add more censorship, to the point that it feels like those base models have more censorship than the closed-source models in ChatGPT.
The fact that they haven't made them multimodal also seems disrespectful to me. If they're going to launch a new model this year, it should have a default view. It's outrageous that in 2026 there are still text-only models. But hey, OpenAI is basically Apple in AI, and if Apple keeps releasing phones with a single camera and a 60Hz screen, why wouldn't OpenAI release models without multimodality functionality? XD | 1 | 0 | 2026-03-02T19:04:36 | Samy_Horny | false | null | 0 | o8a0dpt | false | /r/LocalLLaMA/comments/1rj0mxt/why_are_people_so_quick_to_say_closed_frontiers/o8a0dpt/ | false | 1 |
t1_o8a0ars | I would leave twitter if you dont want to see engagement bait lol | 1 | 0 | 2026-03-02T19:04:13 | Frequent-Mud8705 | false | null | 0 | o8a0ars | false | /r/LocalLLaMA/comments/1rj0mxt/why_are_people_so_quick_to_say_closed_frontiers/o8a0ars/ | false | 1 |
t1_o8a05rd | both bartowski and unsloth just updated their available 27b models. Qwen3.5 small models dropped today. Looking forward to future updates if you are so inclined. Thank you! | 1 | 0 | 2026-03-02T19:03:32 | -_Apollo-_ | false | null | 0 | o8a05rd | false | /r/LocalLLaMA/comments/1rg4zqv/followup_qwen3535ba3b_7_communityrequested/o8a05rd/ | false | 1 |
t1_o8a01t4 | Both. | 2 | 0 | 2026-03-02T19:03:01 | jslominski | false | null | 0 | o8a01t4 | false | /r/LocalLLaMA/comments/1rj0m27/qwen35_2b_4b_and_9b_tested_on_raspberry_pi5/o8a01t4/ | false | 2 |
t1_o8a001j | used qwen 3.5 27b fp16 to finish claude tasks. 100% completed. Python + webapp | 1 | 0 | 2026-03-02T19:02:47 | LegacyRemaster | false | null | 0 | o8a001j | false | /r/LocalLLaMA/comments/1rirlyb/qwenqwen359b_hugging_face/o8a001j/ | false | 1 |
t1_o89zyou | Hi there, as kind of a noob in this area, considering your systems specs - I should also be able to run it on my 16GB 9070XT right? Or is it going to suck cause of missing cuda cores?
I've been dabbling in learning java and using ai (claude and chatgpt) to help where I struggle to understand stuff or find solutions in the past 2 months for a private purpose and was astonished how good this works even for "low-skilled" programmers as myself.
I would love to use my own hardware though and ditch those cloude services even if its going to impact performance and quality a little.
I've got llama running with whisper.cpp locally but as far as I had researched I was left to believe that using local models for coding would be a subpar experience. | 1 | 0 | 2026-03-02T19:02:36 | Pr0tuberanz | false | null | 0 | o89zyou | false | /r/LocalLLaMA/comments/1riwy9w/is_qwen359b_enough_for_agentic_coding/o89zyou/ | false | 1 |
t1_o89zxws | I'm clawcruising for a clawbruising | 1 | 0 | 2026-03-02T19:02:30 | luncheroo | false | null | 0 | o89zxws | false | /r/LocalLLaMA/comments/1rd8nr7/andrej_karpathy_survived_the_weekend_with_the/o89zxws/ | false | 1 |
t1_o89zwxp | the part that resonates most is the difference between data and judgment. ive been building with agents for a while and the frustrating thing is they make decisions that are technically correct but contextually wrong. like an agent will suggest the most popular library for a task when i know from experience that library has a maintainer who disappears every 6 months
for your question 2, i think structured retrieval is the pragmatic starting point. fine tuning on personal data sounds cool but the failure mode is way worse, you get a model thats confidently wrong in YOUR specific way instead of generically wrong. at least with retrieval you can inspect whats being pulled and fix it
the creepiness problem is real but i think its less about local vs cloud and more about whether people trust that the system wont be used against them later. fully local helps but the real barrier is organizational not technical | 1 | 0 | 2026-03-02T19:02:22 | Pitiful-Impression70 | false | null | 0 | o89zwxp | false | /r/LocalLLaMA/comments/1rj1sbq/ai_agents_dont_have_a_context_problem_they_have_a/o89zwxp/ | false | 1 |
t1_o89zuwi | Can you share your command | 1 | 0 | 2026-03-02T19:02:07 | texasdude11 | false | null | 0 | o89zuwi | false | /r/LocalLLaMA/comments/1rii2pd/current_state_of_qwen35122ba10b/o89zuwi/ | false | 1 |
t1_o89zujf | Yeah the hardware struggle is real, I feel that. It's honestly part of the reason I mess around on stuff like NyxPortal.com, just to test out different models without having to deal with the local setup hassle. You're definitely deep in the weeds on the parsing complexities, though. | 1 | 0 | 2026-03-02T19:02:04 | Defro777 | false | null | 0 | o89zujf | false | /r/LocalLLaMA/comments/1riwhcf/psa_lm_studios_parser_silently_breaks_qwen35_tool/o89zujf/ | false | 1 |
t1_o89zsww | can this be used for target seeking missiles? Asking for a friend. | 1 | 0 | 2026-03-02T19:01:51 | tengo_harambe | false | null | 0 | o89zsww | false | /r/LocalLLaMA/comments/1rizodv/running_qwen_35_08b_locally_in_the_browser_on/o89zsww/ | false | 1 |
t1_o89zqjs | I have Qwen3.5-27B q3_k_m working with 65536 context q8_0 cache on my 9070xt. 300tps pp and 27 tps tg. It is crazy good for 16gb. I am happy. | 1 | 0 | 2026-03-02T19:01:31 | hp1337 | false | null | 0 | o89zqjs | false | /r/LocalLLaMA/comments/1rj1ni2/gpu_poor_folks16gb_whats_your_setup_for_coding/o89zqjs/ | false | 1 |
t1_o89znob | Both good catches.
On deduplication: we ran two passes, exact hash dedup followed by semantic embedding similarity (LaBSE + FAISS, cosine threshold 0.92) at both the corpus and Q&A generation levels. The 0.92 threshold is tight enough that it catches most near-duplicate Russian translations of the same passage. LaBSE is cross-lingual so semantic equivalence across translations does register. But you're right that it wasn't explicitly designed for the cross-translation case, and some stylistically distinct retranslations of the same Chrysostom homily probably survived. There are also a lot of citations in Patristic texts, so that may be difficult to remove. The 3.4% removal rate on Q&A pairs (4,189 from 124K) suggests the corpus was cleaner than expected, but that number could be higher with a translation-aware approach.
On tokenizer: we used Qwen2.5's existing vocab as-is. The Cyrillic coverage is genuinely good, Church Slavonic loanwords and theological terminology like θεωρία/θέωσις in transliteration tokenize reasonably well without extension. The main gap is untransliterated Greek and the occasional Latin. Those get subword-fragmented. We considered extending the vocab for high-frequency patristic terms but the tradeoff of re-initializing embeddings for new tokens on a 3B model felt riskier than just letting the existing vocab absorb it, especially since the \~98% Russian corpus would naturally reinforce the Cyrillic token representations during CPT anyway.
What approach did you use for tokenizer extension in your project? | 1 | 0 | 2026-03-02T19:01:08 | Financial-Fun-8930 | false | null | 0 | o89znob | false | /r/LocalLLaMA/comments/1ribjum/i_trained_a_3b_patristic_theology_llm_on_a_single/o89znob/ | false | 1 |
t1_o89zmk6 | Don’t code with <16GB and a local model, lol. Not yet. | 1 | 0 | 2026-03-02T19:00:59 | Usual-Orange-4180 | false | null | 0 | o89zmk6 | false | /r/LocalLLaMA/comments/1rj1ni2/gpu_poor_folks16gb_whats_your_setup_for_coding/o89zmk6/ | false | 1 |
t1_o89zgc3 | Things change fast. Qwen does regular model releases, and Qwen 3.5 series is great step forward compared to previous Qwen models, and better integrated vision support is great too.
Other labs also have major updates relatively often. GLM-5 was a great recent release for example. Before that, Kimi K2 Thinking (released in November last year) was deprecated in January with release of K2.5 with vision support and much better long context awareness.
But Qwen3.5 stands out because they offer large family of models, from small 0.8B to large 397B, and many models in between. | 1 | 0 | 2026-03-02T19:00:09 | Lissanro | false | null | 0 | o89zgc3 | false | /r/LocalLLaMA/comments/1rj0mxt/why_are_people_so_quick_to_say_closed_frontiers/o89zgc3/ | false | 1 |
t1_o89zfax | I guess its because you can build better dataset over time as model evolves. | 1 | 0 | 2026-03-02T19:00:01 | SGmoze | false | null | 0 | o89zfax | false | /r/LocalLLaMA/comments/1rirtyy/qwen35_9b_and_4b_benchmarks/o89zfax/ | false | 1 |
t1_o89zb6e | Attention is all you need | 1 | 0 | 2026-03-02T18:59:26 | No_Cantaloupe6900 | false | null | 0 | o89zb6e | false | /r/LocalLLaMA/comments/1aq14kg/understanding_embeddings/o89zb6e/ | false | 1 |
t1_o89z9x3 | For 35B it's good, but I just realized that bartowski/Qwen\_Qwen3.5-4B-GGUF:IQ4\_XS works much better for 4B than the Q3\_K\_XL quant i used above. Better reasoning. | 1 | 0 | 2026-03-02T18:59:16 | AppealSame4367 | false | null | 0 | o89z9x3 | false | /r/LocalLLaMA/comments/1riwy9w/is_qwen359b_enough_for_agentic_coding/o89z9x3/ | false | 1 |
t1_o89z9s0 | Claude? | 1 | 0 | 2026-03-02T18:59:15 | Altruistwhite | false | null | 0 | o89z9s0 | false | /r/LocalLLaMA/comments/1rirtyy/qwen35_9b_and_4b_benchmarks/o89z9s0/ | false | 1 |
t1_o89z9bl | I'm calling overfitted bullshit on closed and open source. Especially for ultra small modles (>10B) that "beat" full models in whatever. It's just cap and hinders development for real tasks. | 1 | 0 | 2026-03-02T18:59:11 | Technical-Earth-3254 | false | null | 0 | o89z9bl | false | /r/LocalLLaMA/comments/1rj0mxt/why_are_people_so_quick_to_say_closed_frontiers/o89z9bl/ | false | 1 |
t1_o89yy1k | the 122b is good match in terms of size to pro 600 and it is fast, though minimax is quite a bit better if there is a combo of 5090 + pro 6000 with 128 g vram in total. the prompt processing and token generation speed is about the same in both models at least here. | 1 | 0 | 2026-03-02T18:57:43 | MinimumCourage6807 | false | null | 0 | o89yy1k | false | /r/LocalLLaMA/comments/1riz0db/qwen35_397ba17b_1bit_quantization_udtq1_0_vs_27b/o89yy1k/ | false | 1 |
t1_o89ywqv | i've lost count of the number of papers that are unreproducible. | 1 | 0 | 2026-03-02T18:57:33 | One-Employment3759 | false | null | 0 | o89ywqv | false | /r/LocalLLaMA/comments/1rj0mxt/why_are_people_so_quick_to_say_closed_frontiers/o89ywqv/ | false | 1 |
t1_o89yqvp | Look into llama-server, it comes with llama.cpp which you can install with homebrew. You should just have to host the model with llama server and update your endpoint to use localhost:8080 or something | 1 | 0 | 2026-03-02T18:56:47 | -Django | false | null | 0 | o89yqvp | false | /r/LocalLLaMA/comments/1riyi54/i_am_using_qwen_ai_model_for_openclaw_and_i/o89yqvp/ | false | 1 |
t1_o89yqn1 | Endless thinking and 100% hallucinated facts. (4bit quant MLX conversion with 12tk/s on Apple M1) | 1 | 0 | 2026-03-02T18:56:46 | Synor | false | null | 0 | o89yqn1 | false | /r/LocalLLaMA/comments/1rirlyb/qwenqwen359b_hugging_face/o89yqn1/ | false | 1 |
t1_o89ymvn | The brain is small as a pee :P | 1 | 0 | 2026-03-02T18:56:17 | PhotographerUSA | false | null | 0 | o89ymvn | false | /r/LocalLLaMA/comments/1rizjco/qwen3508b_released_today_speed_is_insane_157tksec/o89ymvn/ | false | 1 |
t1_o89ymkh | I'm using minimax m2.5 with a combo of 5090 + rtx pro 600 in iq\_4\_xs. It is a blast, with token generation of arounf 100t/s and quality very good. So I would suggest to keep also the 5090 :D. | 1 | 0 | 2026-03-02T18:56:14 | MinimumCourage6807 | false | null | 0 | o89ymkh | false | /r/LocalLLaMA/comments/1riz0db/qwen35_397ba17b_1bit_quantization_udtq1_0_vs_27b/o89ymkh/ | false | 1 |
t1_o89ymdt | I've used menchmaxxed ai, fell for them lots of times back when people were posting them here and making wild claims. You could tell within a few minutes that they weren't really that smart tho so we shall see.
| 1 | 0 | 2026-03-02T18:56:13 | ArchdukeofHyperbole | false | null | 0 | o89ymdt | false | /r/LocalLLaMA/comments/1rj0mxt/why_are_people_so_quick_to_say_closed_frontiers/o89ymdt/ | false | 1 |
t1_o89yl5n | Genuinely curious, who uses those kinds of models? I've never ever seen a model refusal in my everyday usage, while for "roleplay" there's specific finetunes with enhanced creativity and "specific knowledge". Are there so much unethical hackers so that their finetunes are all of the HuggingFace, or what? | 1 | 0 | 2026-03-02T18:56:03 | No-Refrigerator-1672 | false | null | 0 | o89yl5n | false | /r/LocalLLaMA/comments/1rixh53/qwen35122b_heretic_ggufs/o89yl5n/ | false | 1 |
t1_o89yk06 | Amex Black | 1 | 0 | 2026-03-02T18:55:55 | btc_maxi100 | false | null | 0 | o89yk06 | false | /r/LocalLLaMA/comments/1rj12me/how_are_you_handling_spending_controls_for_your/o89yk06/ | false | 1 |
t1_o89yatn | When you say
> worse results than 2 bit quants of Qwen3.5 a3b
is that referring to generation speed, quality of output, or both? | 1 | 0 | 2026-03-02T18:54:43 | ryrothedino | false | null | 0 | o89yatn | false | /r/LocalLLaMA/comments/1rj0m27/qwen35_2b_4b_and_9b_tested_on_raspberry_pi5/o89yatn/ | false | 1 |
t1_o89y9jh | Personally for me is a great way to play with AI on CPU only with low ram 16GB 2B at Q4\_M uses around 3GB ram. | 1 | 0 | 2026-03-02T18:54:34 | OrdinaryTransition57 | false | null | 0 | o89y9jh | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o89y9jh/ | false | 1 |
t1_o89y9dv | That is indeed harsh feedback | 1 | 0 | 2026-03-02T18:54:32 | -Django | false | null | 0 | o89y9dv | false | /r/LocalLLaMA/comments/1rj18h4/built_a_local_memory_layer_for_ai_agents_where/o89y9dv/ | false | 1 |
t1_o89y8u7 | Just asked why not include it, I will 100% use 0.8b because I have RPi 3b+ with 1GB of RAM | 1 | 0 | 2026-03-02T18:54:28 | stopbanni | false | null | 0 | o89y8u7 | false | /r/LocalLLaMA/comments/1rj0m27/qwen35_2b_4b_and_9b_tested_on_raspberry_pi5/o89y8u7/ | false | 1 |
t1_o89y7mf | Thanks for the heads up. Last time I tried the geohot driver was more than a year ago and had some UI issues. Since then I'm using the dual RTX in a headless setting, so it might be worth another shot. | 1 | 0 | 2026-03-02T18:54:18 | Sufficient-Rent6078 | false | null | 0 | o89y7mf | false | /r/LocalLLaMA/comments/1rianwb/running_qwen35_27b_dense_with_170k_context_at/o89y7mf/ | false | 1 |
t1_o89y785 | [deleted] | 1 | 0 | 2026-03-02T18:54:15 | [deleted] | true | null | 0 | o89y785 | false | /r/LocalLLaMA/comments/1riwy9w/is_qwen359b_enough_for_agentic_coding/o89y785/ | false | 1 |
t1_o89y0v6 | [removed] | 1 | 0 | 2026-03-02T18:53:26 | [deleted] | true | null | 0 | o89y0v6 | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o89y0v6/ | false | 1 |
t1_o89xyou | completely agree | 1 | 0 | 2026-03-02T18:53:09 | Distinct_Track_5495 | false | null | 0 | o89xyou | false | /r/LocalLLaMA/comments/1riboy2/learnt_about_emergent_intention_maybe_prompt/o89xyou/ | false | 1 |
t1_o89xx2k | exactly | 1 | 0 | 2026-03-02T18:52:57 | Distinct_Track_5495 | false | null | 0 | o89xx2k | false | /r/LocalLLaMA/comments/1riboy2/learnt_about_emergent_intention_maybe_prompt/o89xx2k/ | false | 1 |
t1_o89xwnl | [bruh even the Google sheets are scuffed in any view I can get](https://i.imgur.com/1UQ20xa.jpeg) | 1 | 0 | 2026-03-02T18:52:54 | letsgoiowa | false | null | 0 | o89xwnl | false | /r/LocalLLaMA/comments/1rivckt/visualizing_all_qwen_35_vs_qwen_3_benchmarks/o89xwnl/ | false | 1 |
t1_o89xv4b | Clairement | 1 | 0 | 2026-03-02T18:52:42 | No_Cantaloupe6900 | false | null | 0 | o89xv4b | false | /r/LocalLLaMA/comments/1dzqa6s/how_are_embeddings_trained/o89xv4b/ | false | 1 |
t1_o89xs7d | git clone https://github.com/yourusername/yourmemory
> /yourusername/
take your vibecoded shit and get the fuck out | 1 | 0 | 2026-03-02T18:52:20 | MelodicRecognition7 | false | null | 0 | o89xs7d | false | /r/LocalLLaMA/comments/1rj18h4/built_a_local_memory_layer_for_ai_agents_where/o89xs7d/ | false | 1 |
t1_o89xrp0 | First find out which model are you willing to run according to your demands. You can try models online (qwen, z-ai, minimax, etc), once you find out, look for the hardware that is needed to run it. | 1 | 0 | 2026-03-02T18:52:16 | dionisioalcaraz | false | null | 0 | o89xrp0 | false | /r/LocalLLaMA/comments/1ri635s/13_months_since_the_deepseek_moment_how_far_have/o89xrp0/ | false | 1 |
t1_o89xnhf | You know, sometimes you want to launch a web app, right? So you could either click the bookmark you made to that webapp in the browser, right? But you know what you also could do? Buy 4x RTX3090, install linux, mess for 3 days to get cuda working, compile llama-cpp from source, try to get docker running, run open-claw and then ask reddit why it gives an error message. But in the end it should also allow you to launch the web apps. | 1 | 0 | 2026-03-02T18:51:43 | chris_0611 | false | null | 0 | o89xnhf | false | /r/LocalLLaMA/comments/1riyi54/i_am_using_qwen_ai_model_for_openclaw_and_i/o89xnhf/ | false | 1 |
t1_o89xejz | I checked the tiny ones in lineage-bench (27B for scale):
|Nr|model\_name|lineage|lineage-8|lineage-64|lineage-128|lineage-192|
|:-|:-|:-|:-|:-|:-|:-|
|1|qwen/qwen3.5-27b|0.944|1.000|1.000|0.925|0.850|
|2|qwen/qwen3.5-9b|0.556|1.000|0.775|0.275|0.175|
|3|qwen/qwen3.5-4b|0.469|1.000|0.650|0.175|0.050|
There seems to be a spark of intellect still present in 9B and 4B. | 1 | 0 | 2026-03-02T18:50:35 | fairydreaming | false | null | 0 | o89xejz | false | /r/LocalLLaMA/comments/1rirtyy/qwen35_9b_and_4b_benchmarks/o89xejz/ | false | 1 |
t1_o89xedv | It does | 1 | 0 | 2026-03-02T18:50:34 | suicidaleggroll | false | null | 0 | o89xedv | false | /r/LocalLLaMA/comments/1riz7dv/unslothqwen359bggufq8_0_failing_on_ollama/o89xedv/ | false | 1 |
t1_o89xcph | I asked it to do research on what are the best money making app ideas and then asked it to build them for.me | 1 | 0 | 2026-03-02T18:50:21 | utsavsarkar | false | null | 0 | o89xcph | false | /r/LocalLLaMA/comments/1riyi54/i_am_using_qwen_ai_model_for_openclaw_and_i/o89xcph/ | false | 1 |
t1_o89x90j | Yes but this is nearly a daily occurrence by now and was explained a million times. The people that post this shit could literally just scroll down a bit to see it was already posted but they dont ... | 1 | 0 | 2026-03-02T18:49:53 | Finanzamt_Endgegner | false | null | 0 | o89x90j | false | /r/LocalLLaMA/comments/1riy7cw/lmao/o89x90j/ | false | 1 |
t1_o89x63g | Intresing, I'll check that out. | 1 | 0 | 2026-03-02T18:49:31 | kindofbluetrains | false | null | 0 | o89x63g | false | /r/LocalLLaMA/comments/1riv3wv/qwen_35_2b_on_android/o89x63g/ | false | 1 |
t1_o89x3oy | and is it prod ready like vllm ? i have rtx 5070 ti and vllm has some issues with latest arch. and fix is still not out for that one | 1 | 0 | 2026-03-02T18:49:12 | callmedevilthebad | false | null | 0 | o89x3oy | false | /r/LocalLLaMA/comments/1riz7dv/unslothqwen359bggufq8_0_failing_on_ollama/o89x3oy/ | false | 1 |
t1_o89x2a8 | I mean, I have zero looping ? Nada !
llama.server.exe -m E:\\LLMa\_Models\\Huihui-Qwen3.5-35B-A3B-abliterated.Q5\_K\_S.gguf --mmproj E:\\LLMa\_Models\\mmproj-BF16.gguf --port 1337 --host [127.0.0.1](http://127.0.0.1) \-c 40960 -ngl 49 -fa on -ctk q8\_0 -ctv q8\_0 --samplers top\_k;temperature --sampling-seq kt --top-k 80 --temp 0.8
this is how I run mine on a 5090 | 1 | 0 | 2026-03-02T18:49:01 | Not4Fame | false | null | 0 | o89x2a8 | false | /r/LocalLLaMA/comments/1riunee/how_to_fix_endless_looping_with_qwen35/o89x2a8/ | false | 1 |
t1_o89wwzi | It's not a bug, as such, just that when a smaller model doesn't have the capacity to predict a complex pattern it often "falls back" to repetition (which is a very easy pattern to learn, and slightly better than no-skill).
Qwen 3 was okay, even at 30BA3B or 4B, but did have this problem on difficult documents in my testing. Haven't run 3.5 yet. | 1 | 0 | 2026-03-02T18:48:20 | the__storm | false | null | 0 | o89wwzi | false | /r/LocalLLaMA/comments/1rivzcl/qwen_35_2b_is_an_ocr_beast/o89wwzi/ | false | 1 |
t1_o89wx3h | Thanks for the comment. Really appreciate. Let me explore if openwebui works with "llama.cpp behind llama-swap". | 1 | 0 | 2026-03-02T18:48:20 | callmedevilthebad | false | null | 0 | o89wx3h | false | /r/LocalLLaMA/comments/1riz7dv/unslothqwen359bggufq8_0_failing_on_ollama/o89wx3h/ | false | 1 |
t1_o89wrsk | 0.8B = 800M .
now you know why! | 1 | 0 | 2026-03-02T18:47:39 | kayteee1995 | false | null | 0 | o89wrsk | false | /r/LocalLLaMA/comments/1rizjco/qwen3508b_released_today_speed_is_insane_157tksec/o89wrsk/ | false | 1 |
t1_o89wqnb | Reminds me of gemini 3 flash being far superior at chess than the thinking version and other flag ship thinking models at the time | 1 | 0 | 2026-03-02T18:47:31 | EclecticAcuity | false | null | 0 | o89wqnb | false | /r/LocalLLaMA/comments/1rivckt/visualizing_all_qwen_35_vs_qwen_3_benchmarks/o89wqnb/ | false | 1 |
t1_o89wpei | 122B with FP4 would be perfection for RTX Pro 6000. | 1 | 0 | 2026-03-02T18:47:21 | Expensive-Paint-9490 | false | null | 0 | o89wpei | false | /r/LocalLLaMA/comments/1riz0db/qwen35_397ba17b_1bit_quantization_udtq1_0_vs_27b/o89wpei/ | false | 1 |
t1_o89wn8e | Can you turn reasoning off in ollama? | 1 | 0 | 2026-03-02T18:47:04 | Mashic | false | null | 0 | o89wn8e | false | /r/LocalLLaMA/comments/1rivzcl/qwen_35_2b_is_an_ocr_beast/o89wn8e/ | false | 1 |
t1_o89wh2h | Qwen3.5-0.8B-Q8\_0.gguf: with 16k context, one sentence prompt and 500 token output I got 8.05t/s without using SSD. | 1 | 0 | 2026-03-02T18:46:16 | jslominski | false | null | 0 | o89wh2h | false | /r/LocalLLaMA/comments/1rj0m27/qwen35_2b_4b_and_9b_tested_on_raspberry_pi5/o89wh2h/ | false | 1 |
t1_o89wf2n | If you decide to keep it going I would be up for contributing / testing | 1 | 0 | 2026-03-02T18:46:01 | Initial-Argument2523 | false | null | 0 | o89wf2n | false | /r/LocalLLaMA/comments/1rj08k1/k2_not_25_distillation_still_worth_it/o89wf2n/ | false | 1 |
t1_o89wbv6 | You can see how qwen3-VL transformers handle video: [https://github.com/huggingface/transformers/tree/main/src/transformers/models/qwen3\_vl](https://github.com/huggingface/transformers/tree/main/src/transformers/models/qwen3_vl) The Qwen3.5 tranformers are also there, in the qwen3\_5 directories.
I'm not sure if any of the backends (llama.cpp, ollama, lmstudio, etc.) have video implemented.
I created [llm-python-vision-multi-images.py](https://github.com/Jay4242/llm-scripts/blob/main/llm-python-vision-multi-images.py) to be able to send an arbitrary number of frames to the bot at a time. I've been using it with [llm-ffmpeg-edit.bash](https://github.com/Jay4242/llm-scripts/blob/main/llm-ffmpeg-edit.bash) to step through video 10 seconds at a time at 2 FPS by default. You can technically do whatever fits in context though.
Any other video options are going to be doing *basically* the same thing, chopping the video into frames, maybe transcribing the audio, and organizing things in context somehow. Qwen-Omni series also have the audio multimodality, but Qwen3-Omni never got llama.cpp support for reasons beyond my understanding. | 1 | 0 | 2026-03-02T18:45:36 | SM8085 | false | null | 0 | o89wbv6 | false | /r/LocalLLaMA/comments/1riv5kc/whats_possible_with_video_now/o89wbv6/ | false | 1 |
t1_o89w76f | Why would this even matter? Isn't LM Studio just a GUI running something like that under the hood? | 1 | 0 | 2026-03-02T18:45:00 | ArkCoon | false | null | 0 | o89w76f | false | /r/LocalLLaMA/comments/1rirlyb/qwenqwen359b_hugging_face/o89w76f/ | false | 1 |
t1_o89w4vc | It should, it will just take a bit more time. Same for embedding models, and TTS and ASR (audio to speech and viceversa). | 1 | 0 | 2026-03-02T18:44:42 | guesdo | false | null | 0 | o89w4vc | false | /r/LocalLLaMA/comments/1rirtyy/qwen35_9b_and_4b_benchmarks/o89w4vc/ | false | 1 |
t1_o89w4r3 | Nice, it's not even working in Alibaba's own MNN Chat yet--just crashes every time. | 1 | 0 | 2026-03-02T18:44:41 | DeProgrammer99 | false | null | 0 | o89w4r3 | false | /r/LocalLLaMA/comments/1riv3wv/qwen_35_2b_on_android/o89w4r3/ | false | 1 |
t1_o89vxep | if the error says "unsupported arch" then compile latest from source, first version that supported the qwen35 architecture is less than a month old. | 1 | 0 | 2026-03-02T18:43:47 | quilso | false | null | 0 | o89vxep | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o89vxep/ | false | 1 |
t1_o89vwnl | Old model? It hasn't even been out for a year, but oh well, those models don't even have vision to begin with. | 1 | 0 | 2026-03-02T18:43:41 | Samy_Horny | false | null | 0 | o89vwnl | false | /r/LocalLLaMA/comments/1rj0mxt/why_are_people_so_quick_to_say_closed_frontiers/o89vwnl/ | false | 1 |
t1_o89vjyc | > hi
<send>
> What did he mean by "hi"? Wait a minute, what do any of us ever mean by that word? Or is it a phrase? Anyway usually it's a friendly tone, so maybe I should say hi back. Nah that's too simple, I'm a sophisticated thinking LLM. Better dig into the philosophical underpinnings of short un-grammatical phrases and work back to a discrete distribution of the user's intent, choosing the maximum likelihood from there to construct a well-reasoned response. | 1 | 0 | 2026-03-02T18:42:03 | Much-Researcher6135 | false | null | 0 | o89vjyc | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o89vjyc/ | false | 1 |
t1_o89vjoh | Small models are perfect for edge devices and local processing! I use them for quick text classification, sentiment analysis, and even as coding assistants on my laptop without needing cloud access. The quantized versions run super fast on CPU-only setups, which is great for privacy-sensitive tasks or when you're offline. Plus they're amazing for prototyping before scaling up to larger models. | 1 | 0 | 2026-03-02T18:42:01 | Vey_TheClaw | false | null | 0 | o89vjoh | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o89vjoh/ | false | 1 |
t1_o89vfqm | Finetune it with symbolic semantic graphs and go intent golden tokens saved approach | 1 | 0 | 2026-03-02T18:41:31 | fab_space | false | null | 0 | o89vfqm | false | /r/LocalLLaMA/comments/1rizlkn/qwen_27b_is_a_beast_but_not_for_agentic_work/o89vfqm/ | false | 1 |
t1_o89vcbk | GPT-OSS is old MoE model, Qwen3.5 is very recent, and 9B is a dense model, so it should easily beat old GPT-OSS 20B MoE easily in most areas. GPT-OSS 120B still may have greater world knowledge though compared to 9B, but it is still old model, so it makes sense it lags behind by now. | 1 | 0 | 2026-03-02T18:41:04 | Lissanro | false | null | 0 | o89vcbk | false | /r/LocalLLaMA/comments/1rj0mxt/why_are_people_so_quick_to_say_closed_frontiers/o89vcbk/ | false | 1 |
t1_o89v9mq | Sorry I can't really help with this specific question, I can just offer advice to move on from Ollama. It's slow and unreliable, and the devs don't care about it anymore (they've switched focus to their cloud offerings).
Take a few hours to research the alternatives and spin one up, my guess is this and other problems will disappear when you do. I'm a fan of llama.cpp behind llama-swap personally, other people prefer vLLM or SGLang, but really anything is better than Ollama. | 1 | 0 | 2026-03-02T18:40:44 | suicidaleggroll | false | null | 0 | o89v9mq | false | /r/LocalLLaMA/comments/1riz7dv/unslothqwen359bggufq8_0_failing_on_ollama/o89v9mq/ | false | 1 |
t1_o89v8i2 | Interesting direction. For multi-agent systems, I’d separate memory into canonical facts, role-local working memory, and temporary scratchpads with expiry. If Hippocampus controls retention, I’d benchmark contradiction rate + task success under context shifts (not just token savings). The offload-and-recall fallback is a strong safety valve. | 1 | 0 | 2026-03-02T18:40:35 | xing_horizon | false | null | 0 | o89v8i2 | false | /r/LocalLLaMA/comments/1riz852/what_if_a_small_ai_decided_what_your_llm_keeps_in/o89v8i2/ | false | 1 |
t1_o89v8fq | You are the only one addressing the points I would have liked the discussion would have developed LOL, thanks! | 1 | 0 | 2026-03-02T18:40:34 | dionisioalcaraz | false | null | 0 | o89v8fq | false | /r/LocalLLaMA/comments/1ri635s/13_months_since_the_deepseek_moment_how_far_have/o89v8fq/ | false | 1 |
t1_o89v6gd | > + "turn this into a meme/comic"
That was not needed. Just a screenshot of like 15% of the OP and this part of the comments, including long comment san's "some sort of retirement meme would fit amazingly here". | 1 | 0 | 2026-03-02T18:40:19 | themoregames | false | null | 0 | o89v6gd | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o89v6gd/ | false | 1 |
t1_o89uy63 | Oh that's easy, just add this as an argument: `--chat-template-kwargs "{\"enable_thinking\": false}"` | 1 | 0 | 2026-03-02T18:39:16 | cultoftheilluminati | false | null | 0 | o89uy63 | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o89uy63/ | false | 1 |
t1_o89uu3a | Does this mean that the 27B model is best for coding? | 1 | 0 | 2026-03-02T18:38:45 | fernando782 | false | null | 0 | o89uu3a | false | /r/LocalLLaMA/comments/1rivckt/visualizing_all_qwen_35_vs_qwen_3_benchmarks/o89uu3a/ | false | 1 |
t1_o89utwu | VALIDATION_STATUS.md:
**ascend-compat is simulation-validated, not hardware-validated.**
The architecture, test suite, and patching machinery work correctly in
CPU-fallback mode. The CUDA-to-NPU argument mappings are based on Huawei's
documentation, not empirical NPU execution.
more AI-hallucinated crap | 1 | 0 | 2026-03-02T18:38:44 | MelodicRecognition7 | false | null | 0 | o89utwu | false | /r/LocalLLaMA/comments/1rj0dsf/running_llms_on_huawei_ascend_without_rewriting/o89utwu/ | false | 1 |
t1_o89uovx | The context is coding. Which instruct variant are you suggesting is better than qwen3-next at coding? | 1 | 0 | 2026-03-02T18:38:06 | Terminator857 | false | null | 0 | o89uovx | false | /r/LocalLLaMA/comments/1riwy9w/is_qwen359b_enough_for_agentic_coding/o89uovx/ | false | 1 |
t1_o89uly3 | Actually, that table is just a rounded version of the same raw data I used for the chart (from my [Google Sheet](https://docs.google.com/spreadsheets/d/1A5jmS7rDJe114qhRXo8CLEB3csKaFnNKsUdeCkbx_gM/edit?usp=sharing)).
To keep the chart readable, I averaged the scores into the general categories Qwen uses (Knowledge, Math, Coding, etc.) rather than listing out 25 individual benchmarks. It's not a copy-paste from Artificial Analysis; it's pulled directly from the official Qwen3.5 model cards. | 1 | 0 | 2026-03-02T18:37:43 | Jobus_ | false | null | 0 | o89uly3 | false | /r/LocalLLaMA/comments/1rivckt/visualizing_all_qwen_35_vs_qwen_3_benchmarks/o89uly3/ | false | 1 |
t1_o89ugbj | It's seems so close, and not much further from perfect than other solutions I've tried. I commend their work on the front end, though, and that the work was balanced between the front and back offering a lot to both in 0.4.x. Add mcp/youtube-transcript and mcp/webfetch and qwen3.5 (with thinking turned off), and with the semi-persistent KV cache it's an amazing deep researcher up into 64Ktoken+ context windows even on an old RTX-3090. | 1 | 0 | 2026-03-02T18:36:59 | One-Cheesecake389 | false | null | 0 | o89ugbj | false | /r/LocalLLaMA/comments/1riwhcf/psa_lm_studios_parser_silently_breaks_qwen35_tool/o89ugbj/ | false | 1 |
t1_o89ucd5 | Highly autonomous potatoes! | 1 | 0 | 2026-03-02T18:36:28 | Much-Researcher6135 | false | null | 0 | o89ucd5 | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o89ucd5/ | false | 1 |
t1_o89ubbw | Hi writers 👋
I’m one of the people behind **Blocwrite.com**. It started because I kept breaking my own stories.
Once my drafts got long, things would fall apart. A character would show up before being introduced. I’d accidentally repeat the same scene beat in a different chapter. My timeline would drift. My “story bible” was scattered across docs and notes.
Blocwrite isn’t just an AI text generator. It’s a **structured writing studio** built around story control:
* You build your novel **scene by scene (“blocs”)**, so you can actually see the structure.
* It maintains a **living canon/story bible** for characters, locations, lore, and timeline.
* It flags **continuity issues** — like early character appearances, contradictions, or duplicated scenes.
* You can draft with AI if you want, or write completely on your own inside the system.
It’s not credit-based — no watching a token counter while you think. The goal is to reduce plot sprawl and blank-page paralysis so you can focus on telling the story.
If you’ve ever gotten 40k words in and realized your own plot doesn’t make sense anymore, I’d genuinely love to know if this sounds useful — or what you’d want something like this to do better.
You can check it out at **Blocwrite.com**. | 1 | 0 | 2026-03-02T18:36:19 | Afternoon-Doodles | false | null | 0 | o89ubbw | false | /r/LocalLLaMA/comments/1qt2po4/a_list_of_creative_writing_benchmarks/o89ubbw/ | false | 1 |
t1_o89ub4n | It's q2k weak lol | 1 | 0 | 2026-03-02T18:36:18 | PhotographerUSA | false | null | 0 | o89ub4n | false | /r/LocalLLaMA/comments/1ritcfr/imrpove_qwen35_performance_on_weak_gpu/o89ub4n/ | false | 1 |
t1_o89uaev | GLM-OCR is amazing for text, but I have lots of documents with tables, etc.
Qwens are greate in reproducing tables. | 1 | 0 | 2026-03-02T18:36:12 | Pjotrs | false | null | 0 | o89uaev | false | /r/LocalLLaMA/comments/1rivzcl/qwen_35_2b_is_an_ocr_beast/o89uaev/ | false | 1 |
t1_o89u6w3 | You can also easily load them inside of a web application using WebLLM! | 1 | 0 | 2026-03-02T18:35:44 | brandon-i | false | null | 0 | o89u6w3 | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o89u6w3/ | false | 1 |
t1_o89u5n2 | Benchmarks aside, I'm not entirely convinced 110b beats gpt-oss-120b yet though it could just be the fact I can run gpt at native quant vs the qwen quant I had being flawed
27b fails a lot of my own benchmarks that gpt handles as well. So I'm sure a 14b Qwen3.5 will benchmark great, will be fast, and may outperform in some areas, but I wouldn't pin my hopes in it being the solid all-rounder gpt is | 1 | 0 | 2026-03-02T18:35:34 | BigYoSpeck | false | null | 0 | o89u5n2 | false | /r/LocalLLaMA/comments/1riwy9w/is_qwen359b_enough_for_agentic_coding/o89u5n2/ | false | 1 |
t1_o89u4yv | Yes, if you are looking for hints for what to do. No, if you expect the agent to write clean code and not deceive you. | 1 | 0 | 2026-03-02T18:35:28 | Terminator857 | false | null | 0 | o89u4yv | false | /r/LocalLLaMA/comments/1riwy9w/is_qwen359b_enough_for_agentic_coding/o89u4yv/ | false | 1 |
t1_o89twnv | For simple agentic tasks (single-file edits, basic scaffolding), 9B works surprisingly well - I've been using it with Roo Code for quick prototyping. But for multi-step workflows that require maintaining context across 10+ tool calls, it starts to lose coherence around step 5-6.
The sweet spot I found: use 9B for initial exploration and small tasks, then switch to 27B-35B A3B for the actual implementation phase. The MoE models handle long-horizon planning way better while still being runnable on consumer hardware.
Also depends heavily on your quant - Q6_K or higher makes a noticeable difference for tool calling accuracy vs Q4. If you're stuck at 8GB VRAM, try running 35B-A3B with heavy CPU offload. Slower (8-12 t/s) but more reliable than pushing 9B beyond its limits. | 1 | 0 | 2026-03-02T18:34:24 | IulianHI | false | null | 0 | o89twnv | false | /r/LocalLLaMA/comments/1riwy9w/is_qwen359b_enough_for_agentic_coding/o89twnv/ | false | 1 |
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