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r/LocalLLaMA · u/Juulk90871 août 2026, 17:16

Deepseek v4 flash 0731 still not holding up.

The biggest issue with preview was its inability to follow rules prompts and skills. It seems like no matter what you do it ignores them. I've tried first person and second person. I've tried Chinese and English. It does not follow them. That's the only problem with these models and why they're not actually frontier level and not just benchmaxxed. Every user's environment is different and they need to tune the actions and behavior of the model with rules or prompts or skills to exactly what they need to do and if the model ignores then it acts subpar. The newest version of flash has the same issue as the preview version and that's unfortunate.

I run it in native locally. And I've held out making this post cause I know I'm going to get roasted to all fuck but when you can actually run the models locally and when you're not brainwashed by benchmarks and you actually code with them you see the holes. I'm going back to qwen 27b ugh

131

Commentaires

u/RedParaglider21 août 2026, 17:19
This is a valid post I see no reason to flame it maybe you someone out there has some improved template that can help or they have the same problem. I usually don't implement brand new models really quickly because templates are usually spaghetti for a while.
- u/NeedsSomeSnare11 août 2026, 17:44
  "no reason to flame it"... Why are the replies from the top 1% commenters the most disconnected? "Flaming" a post literally just meant many people argued with it, or even just replied to it. That's not the case at all here. Do you just reply to keep your 1% status, regardless of whether your reply actually means anything?
  - u/Borkato11 août 2026, 17:451 réponse
  - u/Anbeeld11 août 2026, 17:501 réponse
  - u/RedParaglider11 août 2026, 17:551 réponse
  - u/[deleted]11 août 2026, 17:56
u/TokenRingAI11 août 2026, 17:22
I'm running it at 3 bit and the model isn't doing well at that quant, it is too lobotomized to be useful. What quant did you try? It's a decent model at full precision
- u/trying4k11 août 2026, 17:26
  What quant are you running at 3bit? And what issues are you seeing?
  - u/TokenRingAI11 août 2026, 17:47
- u/Juulk908711 août 2026, 17:28
  Native. Full precision
- u/EmPips11 août 2026, 17:29
  The previous checkpoint was unusable until you reached the higher Q3 and Q4 levels of quantization. 13B active can only do so much at low precision I guess.
  - u/live4evrr11 août 2026, 18:01
- u/jld153211 août 2026, 17:41
  Which 3 bit? The IQ3_XXS from Unsloth was completely broken on launch and has since been updated. The IQ3_S was noticeably better, though I think there will be updates to all their quants. From my testing the IQ3_S DS4F is closer to Kimi K2.6 level than Qwen3.6 27B.
  - u/TokenRingAI11 août 2026, 17:451 réponse
u/trying4k11 août 2026, 17:23
I had heard someone else talk about this. I'll be curious to hear opinions here since I was considering upgrading hardware to run this. Qwen is great but sucks at design/planning of complex features. I was hoping DS4F helped there. It may help if you put your quant and context as well. Q8 going to be different than Q1.
- u/Juulk908711 août 2026, 17:28
  I'm running full precision native it's in the thread..
  - u/trying4k11 août 2026, 17:36
u/tomz1711 août 2026, 17:25
There are two possibilities here : \- The entire internet is wrong so far \- OP mis-configured their system
- u/Juulk908711 août 2026, 17:27
  Great argument have you ran it locally to try it?
  - u/sebt311 août 2026, 17:373 réponses
  - u/ObviouzFigure11 août 2026, 17:40
  - u/laterbreh11 août 2026, 17:50
- u/Limp_Classroom_264511 août 2026, 17:32
  Misconfiguration is possible if OP is experienced the same issues on a frontier models on his setup, otherwise swapping models on a well configured setup should be enough and it should work out of the box, if it doesn't then the model is not good
  - u/Juulk908711 août 2026, 17:381 réponse
  - u/laterbreh11 août 2026, 17:511 réponse
- u/look11 août 2026, 17:39
  DeepSeek V4 preview has been an extremely popular yet extraordinarily mediocre model for a couple months now. Not really a shocker if the GA follows the same pattern. It’s just *really* cheap and not entirely unusable. Cognitive dissonance in the face of contradictory results keeps it in business … because it is *so* cheap people just want to believe. Simple as that.
  - u/silenceimpaired11 août 2026, 18:22
- u/toothpastespiders11 août 2026, 17:47
  The entire Internet has largely been talking about how well it scores on benchmarks, not actually using the thing. Especially on this sub.
u/ShirtuShanks11 août 2026, 17:31
My time with it more or less aligns with yours. It’s great—noticeably better at following GLM 5.2’s schema—but I tried handing off a fairly straightforward website project’s planning to it and it broke almost all major logic that I’d had set in stone.
- u/nomorebuttsplz11 août 2026, 18:27
  I find it also is slower on Mac Studio than GLM 5.2 getting things done, even though t/s is noticeably faster, because it uses more tokens, gets more confused, and makes more mistakes.
u/fastandlight11 août 2026, 17:31
When you say running native locally, do you mean at Fp8? Could your issues be quant related? Are you quantizing kv cache? I was just looking at spinning up a 4bit quant to test on my infrastructure for agentic use (more reasoning than coding, and some data analysis) based on the benchmark numbers. It seems like it might not be worth it.
- u/Juulk908711 août 2026, 17:35
  I'm saying full precision unquantized. https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731
  - u/fastandlight11 août 2026, 17:38
  - u/Phaelon7411 août 2026, 17:382 réponses
u/BrewHog11 août 2026, 17:35
I'm wondering if it's a harness thing. I still don't understand how I cannot run long horizon tasks with OpenCode, but I can with Pi. OpenCode seems to be what so many YouTubers have success with, but it's just total crap when it comes to oversight. I have to constantly tell it to continue from just stopping, and it restarts a lot of what the process anyway went through. Same model on Pi doesn't have that issue. I'm testing with Openrouter, so not local yet.
- u/justgetoffmylawn11 août 2026, 17:45
  Interesting. I've tried DS4 with OpenCode and been kinda unimpressed, even though I really like using it on both the web (but not for coding there) and the API for my own projects. Tried it briefly with Reasonix, but haven't used Pi.
  - u/BrewHog11 août 2026, 17:52
- u/NandaVegg11 août 2026, 18:22
  It is not easy to break 2026 mid-to-large models significantly at harness level no matter what it does, assuming that everything the model supposed to do can be done easily through bash/terminal. I suspect the difference between OpenCode and pi is just that pi's prefill is very small (around 3000\~4000 tokens) whereas OpenCode is usually larger, which would cause more confuse for smaller active parameter models. However, there are some notable break patterns in more recent model that are heavily RL'd for agentic benchmarks and/or distilled from other closed models. GLM 5.1 was pretty resilient for whatever you do in prefill, but if you mess with GLM 5.2's reasoning prefill (like adding "Let's think it step by step..." at the beginning of reasoning trace) it breaks pretty hard with spitting out more garbage, language drift, etc.
  - u/BrewHog11 août 2026, 18:31
u/MaterialSuspect828611 août 2026, 17:37
Could you confirm if the same behaviour also exists in the official API end point? This might be useful to verify if there are some subtle bugs in the local inference engine you are using
- u/Juulk908711 août 2026, 17:53
  I suppose this could be the case. Maybe the official API endpoint has a different model than what they released for local.
  - u/shing323211 août 2026, 18:001 réponse
- u/BrewHog11 août 2026, 17:56
  I'm not having those issues on the API at all. Most of my use cases are not coding by nature, but require heavy tool use and extensive skills. I'm using Pi as my harness most of the time, but currently testing with OpenCode for this model.
- u/VotZeFuk11 août 2026, 18:27
  IMO DeepSeek kinda messed up by not providing an official Jinja template. Looking at Bartowski and Unsloth .ggufs, they have different Jinjas, one of which is rather barren and the other is full of *'hey-lil-bitch-please-think-hard'* pleas and commands. See for yourself on HF. I mean, I know Unsloth are promoting this as fixes and improvements, but I'd rather take what the actual DeepSeek team has to offer :/ It is truly an unfortunate mess in that regard.
u/segmond11 août 2026, 17:38
How are you using it to code? Which coding harness?
- u/[deleted]11 août 2026, 17:48
  [deleted]
- u/Hoppss11 août 2026, 18:01
  Also curious about this too
u/sdexca11 août 2026, 17:38
Idk I code with them and they follow instructions just fine? Are you sure that you don't have some issue running it locally? Maybe something is wrong with your local environment? Have you tried using the DeepSeek Platform API for once just to compare?
u/LikeSaw11 août 2026, 17:38
I've noticed it when I tried the preview version (haven't tried 0731 yet). Maybe its related to default temperature 1.0 the model being too creative and not following instructions well enough.
- u/trying4k11 août 2026, 17:54
  That seems possible to me. I didn't see anything in the documentation for DS4F but Qwen 3.6 27b uses a value of 1 for more creative output and a value of 0.6 for more strict output. It could be similar here?
  - u/Fluxing_Capacitor11 août 2026, 18:10
u/negus12311 août 2026, 17:39
Seemed pretty good to me? I was running the 8 bit quant too. Had it write up an MCP server for an image model so i could give it “sight”
u/fugogugo11 août 2026, 17:39
what does "does not follow prompt" means?
- u/ttkciar11 août 2026, 18:08
  It means poor instruction-following competence. Models should do what they are instructed to do, but some ignore instructions and make up their own task.
u/Hoak-em11 août 2026, 17:41
I threw it in the same harness that I used to make up for the issues with previous GLM models (GLM-5, GLM-5.1, and in some ways GLM-5.2) which is forgecode. This gives it semantic search, a buncha reminders, an enforced TODOlist and plan -> execute requirement, alongside an automatic tool-fixer. This thing CHUGS like it is amazing how fast it can get through work compared to GLM-5.2. Yeah, it uses a lot more tokens, but the output is the same or better. It seems a lot like the GLM series after 5 to me -- potentially brilliant (opus-level) but ya gotta tie it down at certain points.
u/MrGuest111 août 2026, 17:45
I have it running tp2 with rtx6k in vLLM and it’s amazing so not sure what you’re doing differently. Make sure temp 1 top p 0.95 for coding
- u/TokenRingAI11 août 2026, 17:59
  Which specific quant are you using on RTX 6000?
u/samsteak11 août 2026, 17:46
Curious what others think about this
u/laterbreh11 août 2026, 17:46
I downloaded the model straight from deepseek's hugging face. I swaped the directory for my vllm start script, and ran it through our same agentic loops. Internal measurements for our workloads in the last 24 hours were clearly improved. There must be something wrong on your end. DS4 Flash has been our workhorse and its been reliable and surprising in its ability. Long running long horizon tasks broken into phases and orchestrated cycles with the same model it performs great. Our harnesses are built entirely around opencode.
- u/Fit-Cost-722611 août 2026, 18:29
  damn whats your setup that you can run it locally?
u/No_Medium20511 août 2026, 17:47
If you're not using the official API and the official harness config on their docs it's not valid
- u/ttkciar11 août 2026, 18:06
  If you're using the API then you might be in the wrong subreddit.
u/sullenisme11 août 2026, 17:55
you must've had really bloated contexts. mine stuck to its cardinal rules consistently
u/ResidentPositive412211 août 2026, 18:03
This has been a thing w/ deepseek models since the very first 6b and 7b coders they released in ~23. They are extremely "stubborn", likely due to the heavy RL regime. They are great IF your task closely matches the task types they've been trained on. You want to solve math problems? Great. You want it to analyse a math problem and offer you an analysis on a particular facet of the problem? Tough luck, it will still solve the problem :) (simplified, but that's basically it). What I found w/ dsv4-preview is that you need to let them work in their style. If you want precise rules followed, simplify till they work, and then add another task in another window, to follow "migration" from what's done to how you want done. Also, if you have many rules all over the place, simplify them and have them in one place. Also separate rules by "topic" and have several passes as described above. When you get your tasks in a row and dsv4 "gets it", it's a workhorse. But don't expect to come w/ setups from SotA and have them work 1 for 1. Split, iterate, review, improve. That's how you get them to be useful.
u/MyHobbyIsMagnets11 août 2026, 18:03
Works fine for me
u/wayne_oddstops11 août 2026, 18:04
I've grown used to herding the fucker. System prompts and skills instructions definitely need to be stronger w/ DeepSeek. No hedging language or weak instructions written like they're general advice. Tell it exactly what it should do. If your skills require a certain workflow, list the steps and their order. Don't overload w/ negative "Do not" constraints. Firm positive instructions. Provide varied output examples if your workflow depends on it. I could go on and on. I had to write a skill for Claude just to tell it how to write instructions for DeepSeek.
u/CatchDublinSurprise11 août 2026, 18:04
I have read other reports of a very specific weakness in the DS4-flash model of being unable to maintain separate knowledge and perspectives among characters in creative writing and role play. But I have not seen anything that makes me believe this is a symptom of a broader problem.
- u/silenceimpaired11 août 2026, 18:10
  Do you use models for creative writing? If so what models?
u/shing323211 août 2026, 18:05
if you running local, it does not follow instruction. you should compare official API first before posting your issues. I am guess you are running Sglang？ that particular inference has issue as I heard friend running it and its unusable. so far I have great experience on the official API even comparable to Grok 4.5 or GLM52
u/Potential-Leg-63911 août 2026, 18:09
With a proper orchestrator setup (for example with GLM 5.2) also the „old“ DSV4 Flash was really good. You cant expect, that it can do wveeythin
u/Its_Powerful_Bonus11 août 2026, 18:09
I’m not convinced with any opinions about models which wasn’t tested extensively by users. It’s difficult to believe since yesterday it was possible to check if model behaves as described. I have exactly opposite experience based on refactoring 3 of my side projects and doing a lot of Hermes agent work with ds4 flash yesterday and today. I’m very happy with it comparing to Hy3, ds4 flash preview, minimax m2.7. Qwen 3.6 was also my daily driver when I had to process a lot of information (\~1000 t/s TG is nice with 10-20 concurrent requests) and IMHO Qwen 3.6 27b is very good, but it is another league vs ds4 flash 31.07. Even if ds4 flash is benchmaxed it’s better than anything else now available in similar size. It will be much more reasonable to wait 1 week to come to final conclusions in terms how model behaves. Cheers!
u/jonydevidson11 août 2026, 18:12
You're missing info.

What inference engine?

What harness? Which version?

What system prompt, rules, skills?
u/Potential-Leg-63911 août 2026, 18:13
Via API it seems to be really strong. Running it locally makes no sense to me with that still incredibly cheap prices.
u/Artistic_Okra728811 août 2026, 18:17
My experience is somewhat similar, but I found the harness makes all the difference. A vanilla Pi.dev and both models suck, but Qwen3.6-27B sucks worse. Claude Code in a project repo with memories and planning mode, Qwen3.6-27B works nearly as good as Sonnet 4.5. DeepSeek v4 Flash (preview) seems to ignore most of the setup in the same project and same harness (Claude Code), just changing out the model.
u/BP04111 août 2026, 18:26
I've been testing 0731 through OpenClaw for agentic workflows and yeah, the rule drift is real — even with explicit system prompts reinforced every 8 turns, it starts ignoring constraints around message 12. Separate system prompt that re-injects every N turns helps but it's a bandaid. Might be a positional bias thing with the longer context.
- u/Juulk908711 août 2026, 18:30
  This is exactly the route that I tried to take to fix it as well. I even tries to make a chat template with reminders built into it as well. Everything seems to act as a Band-Aid and it never gives you the confidence to let the agent actually open up.
u/Budget-Juggernaut-6811 août 2026, 18:27
I have a simple pipeline that was using mimo v2.5/mimo v2.5pro for and tried changing to Deepseek V4 flash, but it was failing validation checks for the output.