the identity of the OX Alpha model is Hy4. it is the only new model which could be served at this scale, and new enough and popular for opencode to consider using.
now this is just an assumption, but I am just saying...
Use this when ur considering what model this is!
btw, this is from my good friend johnny: https://huggingface.co/LyJonathan
so as you can see, I have being experimenting with some models, but DAMN, I gotta say, the rate limits are beyond what I expected.
Also, i was kinda forced to buy OpenCode today after hitting the free teir limit (I was gonna buy anyway lol)
but, I gotta say, man – THIS IS AMAZING.
since I am using my ChatGPT Plus subscription with OpenCode Go, I get a great mileage since I mess with a lot of react and rust repos. I have fallen in LOVE with the new deepseek v4 flash and pro, and the qwen3.7 plus model, havn't tried GLM yet, but I am just in love with this harness.
Sorry about the post been a shit post and not been helpful, but I just want to take a moment to appreciate OpenCode for what they give for just $10!
So I was researching some good image generation models (not the first time). and ever f****** time I do this, ChatGPT just generates a stupid image, idk why. its so annoying.
My best conclusion is that ChatGPT detects "generate", and shows this weird ahh image generation preview, and there is a good chance that an image will be created.
Have you gone through the same experience? Any fix?
I told deepseek (with opencode free) to do this:
here is the repo: Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign
can you go look into it and make me a tool where i can geenrate TTS
make an electron app.
themeL plastic, 3d, old vibe theme, the old internet theme!
FYI, this prompt was very vague, short, and had a ton of spelling mistakes, and I am just genuinely surprised a 300B MoE model (which is about ~$0.4 total/M Tok) can do this!
so, OpenCode has this feature where it will show you the reasoning chains of the models
from what I saw, it generates very qwen3.5+ reasoning chain (the structured one)
it does use human thought traces once it break down the prompt, but it fals back to the structured reasonign chin quite often.
so its either:
- glm5.2
- qwen3.8 max
- qwen3.8 27B
considering that GLM AND Qwen3.8 MAX are epxensive models, I assume that the current big pikle model is the qwen3.8 27B model.
I am sorry if the question I asked in the title is a bit stupid, but I see many folks use LM studio, some use ollama, and I personally like Unsloth Studio since I can run GPT OSS 20B at 100 TPS at full context with my 5070 + 32GB DDR5 6k MT/S.
but I want to know if there is a better runtime to use opencode to use it properly. I am somewhat new to OpenCode, so I would like to hear what you guys use with OpenCode!
I got into Codex a few months ago, and something that has bugged me a lot is the loading time.I have a high-end Windows machine (R7 9700x and a 5070) and an M5 MacBook Air, but whenever I load Codex (after quitting from the system tray, btw), it takes a significant time every time!
I mean, surely, a vibecoding app might be complex, but not THAT complex where it takes a solid 20 seconds to load upon a $2000 machine with all the hardware acceleration options turned on.
obv, we can excuse Windows, but the macOS app being this slow is what bugs me :(
Can someone tell me your experience with Codex or any other app like Claude Code or Antigravity? (especially macOS users)
So, I mean qwen3.7 is already good, I have used the 9b model for coding quite a lot, but I wonder if it is THAT good how good will b the qwen4 models?
I really hope it will have controllable reasoning efforts like: none, low, medium, high and xhigh, or else I would just ACTUALLY crash out!
so do yall know soem leaks about it or expected features? I am so excited about qwen4 bro. I mean.. I REALLY wished it had controllable reasoning efforts!
So, these are the top options I get when I search for qwen models on openrouter. I need something which will output its real reasoning chain instead of summarized snapshots like claude or google.
I am using this finetune qwen3.5-2b for a study app I made called oncard. I feel like you should probably read my github readme (I am inactive on it, bcs I am making a remasterof ONCard) repo: https://github.com/MightyXdash/ONCard
Also, I will only have 30 bucks, so I need to get the most of it aswell. I was initially planning to use the qwen3.6/3.7 plus model bcs I can generate like 5-10M tokens total which is a decent amount of samples to do LoRA at around r24 a48. so if someone can help me plan out a decent model for teaching while considering th price would be well appreciated, bcs I could have gone with the max models bcs they are generally the same price as the "okay" GPT or Claude haiku models, but I feel like it is overkill bcs I have used the qwen3.6 plus model in the web app, and I was satisfied with its style.
to help yall get an idea, what my app does is:
- let users upload text based questions/PDF/PPTX/PNGs/and other study material
- the app will put it through a pipeline which will do OCR, and pass the material to the AI to generate something called a paper (which is essentially a research paper like paper)
- the app will take the cleaned data (in a unified structure) and then convert them into JSONs (cards) while considering the amount of questions and difficulty (so we are dealing a lot with JSON in/out and messy data in/json out)
- then those cards will be embedded with either nomic embedd or qwen embedd, then we will take the top different cards and pass it to the text generation model to log it into a pipeline called NNA (which is pipline which will take in the JSON and log into an algorithm to make the next cards the user study more context aware of what the user is currently studying)
so generally, these are what the model has go through:
- Images --> OCR --> OCR + image embedings --> text (long) --> [JSON batches] + [JSON batches]... --> embedd (not related to this, but to clear you guy' mind) --> rerank them --> JSON --> embedd (again, not related, but to make this transparent).
- text in --> JSON --> embedd (not related) --> collect the related one's --> rerank --> log into DB with JSON
then there is this for actual teaching:
- JSON --> JSON --> embedd --> rerank (this will create a very long JSON) --> JSON (to store for future times for the algorithm)
- question --> answer (but teaching focused, and sounding non robotic | probably the easiest task here)
Now, here is the thing, we will be dealing with long JSON prompts and responses, and I found that gemini models perform really well out of the box even with less instructions, and hopefully the cloud qwen models with decent instructions too, bcs qwen3.6 plus was actually decent at this. the qwen3.6 35B model is "okay" but sometimes it is not enough and can be slow, also my 5070 PC will be doing some PT, so I will have to use my macbook.
what I need help with is:
- selecting a good qwen model
- budgeting the usage
- considering the above two, planning the data usage for the 2b model
- also, this is my first vision project, so i am not going to train th qwen3.5-2b model's projector, bcs its actually kinda decent after testing (thats the reason why I am going with this)
thanks for reading this and helping me ot, really appreciate that!
So, these are the top options I get when I search for qwen models on openrouter. I need something which will output its real reasoning chain instead of summarized snapshots like claude or google.
I am using this finetune qwen3.5-2b for a study app I made called oncard. I feel like you should probably read my github readme (I am inactive on it, bcs I am making a remasterof ONCard) repo: https://github.com/MightyXdash/ONCard
Also, I will only have 30 bucks, so I need to get the most of it aswell. I was initially planning to use the qwen3.6/3.7 plus model bcs I can generate like 5-10M tokens total which is a decent amount of samples to do LoRA at around r24 a48. so if someone can help me plan out a decent model for teaching while considering th price would be well appreciated, bcs I could have gone with the max models bcs they are generally the same price as the "okay" GPT or Claude haiku models, but I feel like it is overkill bcs I have used the qwen3.6 plus model in the web app, and I was satisfied with its style.
to help yall get an idea, what my app does is:
- let users upload text based questions/PDF/PPTX/PNGs/and other study material
- the app will put it through a pipeline which will do OCR, and pass the material to the AI to generate something called a paper (which is essentially a research paper like paper)
- the app will take the cleaned data (in a unified structure) and then convert them into JSONs (cards) while considering the amount of questions and difficulty (so we are dealing a lot with JSON in/out and messy data in/json out)
- then those cards will be embedded with either nomic embedd or qwen embedd, then we will take the top different cards and pass it to the text generation model to log it into a pipeline called NNA (which is pipline which will take in the JSON and log into an algorithm to make the next cards the user study more context aware of what the user is currently studying)
so generally, these are what the model has go through:
- Images --> OCR --> OCR + image embedings --> text (long) --> [JSON batches] + [JSON batches]... --> embedd (not related to this, but to clear you guy' mind) --> rerank them --> JSON --> embedd (again, not related, but to make this transparent).
- text in --> JSON --> embedd (not related) --> collect the related one's --> rerank --> log into DB with JSON
then there is this for actual teaching:
- JSON --> JSON --> embedd --> rerank (this will create a very long JSON) --> JSON (to store for future times for the algorithm)
- question --> answer (but teaching focused, and sounding non robotic | probably the easiest task here)
Now, here is the thing, we will be dealing with long JSON prompts and responses, and I found that gemini models perform really well out of the box even with less instructions, and hopefully the cloud qwen models with decent instructions too, bcs qwen3.6 plus was actually decent at this. the qwen3.6 35B model is "okay" but sometimes it is not enough and can be slow, also my 5070 PC will be doing some PT, so I will have to use my macbook.
what I need help with is:
- selecting a good qwen model
- budgeting the usage
- considering the above two, planning the data usage for the 2b model
- also, this is my first vision project, so i am not going to train th qwen3.5-2b model's projector, bcs its actually kinda decent after testing (thats the reason why I am going with this)
thanks for reading this and helping me ot, really appreciate that!
If you search "Chat title dataset" on huggingface a few dys ago, the biggest chat title dataset you would get from it was "ogrnz/chat-titles", but recently at supralabs we have curated a 115K filtered dataset whih breaks the world record for the biggest dataset from 10k samples to 115k samples!
We've released a set of chat title generation datasets that may be useful for instruction tuning, classification-style title generation, or benchmarking small models.
The release includes both a filtered and an unfiltered version:
- Filtered: `SupraLabs/chat-titles-filtered-115K`
- Unfiltered: `SupraLabs/chat-titles-unfiltered-150K`
- Legacy release: `SupraLabs/chat-titles-12K`
The filtered version is the one we generally recommend for most training runs, while the unfiltered version is provided for anyone who prefers to apply their own cleaning and filtering pipeline.
We're interested in hearing feedback from anyone who experiments with the datasets, especially regarding data quality, filtering approaches, and title generation performance across different model sizes.
Questions, suggestions, and criticism are all welcome.
