DedeProGames PRO
DedeProGames
AI & ML interests
Agentic & Coding finetune, Decoder-Onlys from Scratch, Image LoRAs, AI Researcher
Recent Activity
posted an update about 3 hours ago
@DavidAU is officialy humanslop updated a collection about 23 hours ago
🍁 KiyoOrganizations
posted an update about 3 hours ago
replied to Banaxi-Tech's post about 24 hours ago
Also what is Klein?
replied to Banaxi-Tech's post 1 day ago
@Banaxi-Tech You should get ready, because SupraLabs is cooking with Supra2.5. 🔥🔥🔥
reacted to Banaxi-Tech's post with 🚀🔥 1 day ago
Post
1,843
BananaMind 3 is going to be an comeback. Sorry for the BGA things ( i genuinely didnt know about nsa existing and yes I used aiiii)
Were going to use our BananaMind 2.1 Unified architecture with some changes + NSA and some other stuff.
Were going to use our BananaMind 2.1 Unified architecture with some changes + NSA and some other stuff.
- 6 replies
reacted to Banaxi-Tech's post with 😔 2 days ago
Post
4,692
BananaMind is in the google situation before argon right now, right?
- 29 replies
reacted to nightmedia's post with 😔 3 days ago
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2,496
The DavidAU/Qwen3.6-27B-Fable-Fusion-711
This model was assembled at NightmediaAI under the lab name:
nightmedia/Qwen3.6-27B-Architect-Polaris2-Fable-B-F451
There is ample information on the model card about the model itself.
Here are the base model metrics, as it was merged
It is a merge of different models, some abliterated, some as they were, so as a result, this source is not Heretic. DavidAU offers the source in Heretic mode here:
DavidAU/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-MTP
The basic idea behind this model was to build a model that can be used as a base, and has high enough IQ to merge with other models. Every model card shows details of assembly and provides a vibe check with the Nightmedia test prompts.
I merged a few samples here:
with nbeerbower/BigBubba-Qwen3.6-27B
nightmedia/Qwen3.6-27B-Fable-Fusion-711-BigBubba-717-mxfp8-mlx
with migtissera/Tess-4-27B:
nightmedia/Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-Tess-mxfp8-mlx
with EpistemeAI/Reasoning-Medical-27B (metrics pending):
nightmedia/Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-Medical-mxfp8-mlx
This last model was simply a blind merge, and might not be the proper formula for it, but was entertaining enough to publish as a sample.
I am populating the model card metrics as they become available.
This model was assembled at NightmediaAI under the lab name:
nightmedia/Qwen3.6-27B-Architect-Polaris2-Fable-B-F451
There is ample information on the model card about the model itself.
Here are the base model metrics, as it was merged
Qwen3.6-27B-Architect-Polaris2-Fable-B-F451
arc arc/e boolq hswag obkqa piqa wino
mxfp8 0.711,0.879,0.910,0.790,0.514,0.823,0.763
qx86-hi 0.696,0.876,0.912,0.791,0.518,0.824,0.760
qx64-hi 0.702,0.873,0.909,0.794,0.514,0.822,0.750
mxfp4 0.701,0.873,0.909,0.786,0.488,0.813,0.759It is a merge of different models, some abliterated, some as they were, so as a result, this source is not Heretic. DavidAU offers the source in Heretic mode here:
DavidAU/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-MTP
The basic idea behind this model was to build a model that can be used as a base, and has high enough IQ to merge with other models. Every model card shows details of assembly and provides a vibe check with the Nightmedia test prompts.
I merged a few samples here:
with nbeerbower/BigBubba-Qwen3.6-27B
nightmedia/Qwen3.6-27B-Fable-Fusion-711-BigBubba-717-mxfp8-mlx
mxfp8 0.717,0.873,0.912
mxfp4 0.707,0.877,0.912with migtissera/Tess-4-27B:
nightmedia/Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-Tess-mxfp8-mlx
mxfp8 0.712,0.879,0.911,0.792,0.508,0.823,0.764
mxfp4 0.706,0.873,0.910,0.790,0.496,0.817,0.761with EpistemeAI/Reasoning-Medical-27B (metrics pending):
nightmedia/Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-Medical-mxfp8-mlx
mxfp8 0.716,0.877,0.911
mxfp4 0.701This last model was simply a blind merge, and might not be the proper formula for it, but was entertaining enough to publish as a sample.
I am populating the model card metrics as they become available.
- 6 replies
reacted to their post with 🚀 4 days ago
replied to their post 4 days ago
Where is Terminal Bench, SWE, NL2Repo and HLE lol
reacted to their post with 🤝👍🧠🔥👀➕ 5 days ago
replied to their post 6 days ago
Thanks for the suggestion! I ran the CPU benchmark and Portuguese-IFEval. The F16 GGUF scored 6.54% strict prompt accuracy and decoded at 65.5 tokens/s with llama.cpp, versus 10.8 tokens/s in PyTorch FP32. The chat template passed all 9 checks. I couldn’t finish the Q4_K_M quality run, so I don’t have a valid Q4 score yet.
replied to their post 8 days ago
Ent, e dificil ahcar aqui kkkkkk
replied to their post 10 days ago
Im from Brazil
reacted to their post with 🤯 10 days ago
Post
8,876
🚀 Introducing Kiyo-230M-Instruct — my compact English & Portuguese chat model!
Built on Kiyo-230M-Preview, a base model I pretrained from scratch, Kiyo now has an instruction-tuned release for everyday conversations, rewriting, and lightweight assistant experiments.
🧠 229.7M parameters
🌍 English + Portuguese
💬 Native chat template for multi-turn conversations
📏 2,048-token context window
📦 459 MB of BF16 safetensors weights
🔓 Apache-2.0 license
For instruction tuning, I used 165,735 conversations from SmolTalk2, smol-smoltalk, UltraChat, and Dolci-Instruct-SFT. Training ran for two complete epochs with assistant-only loss, using a roughly 70% English / 30% Portuguese token mix.
The updated model card includes benchmarks against SmolLM2-135M-Instruct, BananaMind, and Supra2, plus example conversations, CPU measurements, training details, and a Transformers quickstart.
The results are mixed: Kiyo is competitive with these small models across the reported evaluations, while SmolLM2 and BananaMind score higher on the instruction-following benchmarks. Portuguese was a highlight in the small conversational sanity test.
This is an experimental 230M model, with plenty of room to improve reasoning, factual accuracy, and strict instruction following. I’m excited to share it with people exploring how much we can get from small, accessible models!
🤗 Model & full README:
DedeProGames/Kiyo-230M-Instruct
💬 Try the chat demo:
DedeProGames/kiyo-230m-chat
Try it in English or Portuguese — I’d love to see your examples, feedback, and experiments!
Built on Kiyo-230M-Preview, a base model I pretrained from scratch, Kiyo now has an instruction-tuned release for everyday conversations, rewriting, and lightweight assistant experiments.
🧠 229.7M parameters
🌍 English + Portuguese
💬 Native chat template for multi-turn conversations
📏 2,048-token context window
📦 459 MB of BF16 safetensors weights
🔓 Apache-2.0 license
For instruction tuning, I used 165,735 conversations from SmolTalk2, smol-smoltalk, UltraChat, and Dolci-Instruct-SFT. Training ran for two complete epochs with assistant-only loss, using a roughly 70% English / 30% Portuguese token mix.
The updated model card includes benchmarks against SmolLM2-135M-Instruct, BananaMind, and Supra2, plus example conversations, CPU measurements, training details, and a Transformers quickstart.
The results are mixed: Kiyo is competitive with these small models across the reported evaluations, while SmolLM2 and BananaMind score higher on the instruction-following benchmarks. Portuguese was a highlight in the small conversational sanity test.
This is an experimental 230M model, with plenty of room to improve reasoning, factual accuracy, and strict instruction following. I’m excited to share it with people exploring how much we can get from small, accessible models!
🤗 Model & full README:
DedeProGames/Kiyo-230M-Instruct
💬 Try the chat demo:
DedeProGames/kiyo-230m-chat
Try it in English or Portuguese — I’d love to see your examples, feedback, and experiments!
- 9 replies