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Enderchef (Enderchefcoder) PRO

Enderchef

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Enderchefcoder

AI & ML interests

See my bio at https://github.com/Enderchefcoder

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liked a Space about 5 hours ago

FineEnvs/multi-harness-rl

posted an update about 5 hours ago

Introducing Open SLM Evaluations! Open SLM Evaluations is a dataset containing benchmarks for 106 SLMs(150M parameters and below). It has benchmark scores for each model, links, parameter counts, and names. Results were evaluated independently over 30 GPU minutes on a Runpod B200! Data is open on Apache 2.0! Check it out! https://huggingface.co/datasets/Enderchef/Open-SLM-Evalulations

updated a collection about 6 hours ago

NAXIUM TUNE

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posted an update about 5 hours ago

Post

Introducing Open SLM Evaluations!
Open SLM Evaluations is a dataset containing benchmarks for 106 SLMs(150M parameters and below). It has benchmark scores for each model, links, parameter counts, and names.
Results were evaluated independently over 30 GPU minutes on a Runpod B200!
Data is open on Apache 2.0! Check it out!
Enderchef/Open-SLM-Evalulations

reacted to raincandy-u's post with 👍🔥 about 7 hours ago

Post

1174

20K parameters can tell a story. 🚀

🤗 We trained a ~20k-parameter Transformer that can actually write stories!

raincandy-u/MacroStories

→ ~50× smaller than the 1M-parameter TinyStories model
→ ~3,000× smaller than AlexNet
→ 81 KB in FP32

yayyy the whole model. ૮ ˶ᵔ ᵕ ᵔ˶ ა

She has a 32-dimensional hidden state, a 378-token vocabulary, and just one decoder block — recurrently applied 4 times with shared weights.

Despite having only 19,969 parameters, she can maintain a simple narrative across 100–300 words: establish a goal, encounter a problem, take relevant actions, and reach an outcome.

She runs extremely fast on CPU — no GPU required. The entire model is tiny enough to load almost instantly! ☺️

reacted to CountingSheep's post with 🔥 1 day ago

Post

1692

Vev: Jev-style decisions about images, from a 4B or 9B model you can run yourself.

Give it a screenshot or photo plus a yes/no, multiple-choice, or scoring question, and it returns a probability for every option instead of generating text.

It serves TypeSafe's /v1/systemone format, so the official SDK works by changing the base URL, including requests with images.

The clip shows vev-4b playing Doom in real time. On each look, the image is split into 8 vertical slices, and Vev answers 8 yes/no questions in a single request, one per slice. A small fixed-rule harness turns those probabilities into turning and firing.

Try it in the browser:
CountingSheep/vev

Weights:
CountingSheep/vev-4b
CountingSheep/vev-9b

LoRA adapters are also available in the collection.

Code:
https://github.com/Xiaooolong/vev

Fine-tuned from Qwen3.5. Tested on NVIDIA GPUs so far.

reacted to their post with 🧠 11 days ago

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3040

AxiomicLabs released new benchmark, Tiny Theory of Mind, to test your SLM models' Theory of Mind Intuition!
Check it out and like it!

AxiomicLabs/Tiny_Theory_of_Mind

reacted to ereniko's post with ❤️ 12 days ago

Post

285

NEW CLAUDE HAIKU MODEL COMING

FINALLY AFTER A YEAR

ANTHROPIC ANNOUNCED HAIKU 5.5

reacted to their post with ❤️🔥🚀 13 days ago

Post

3040

AxiomicLabs released new benchmark, Tiny Theory of Mind, to test your SLM models' Theory of Mind Intuition!
Check it out and like it!

AxiomicLabs/Tiny_Theory_of_Mind

posted an update 13 days ago

Post

3040

AxiomicLabs released new benchmark, Tiny Theory of Mind, to test your SLM models' Theory of Mind Intuition!
Check it out and like it!

AxiomicLabs/Tiny_Theory_of_Mind

reacted to ManniX-ITA's post with 👀🚀🔥🔥 24 days ago

Post

700

🚀 JackOD-9B-Coder — a 9B merge built to FINISH agentic coding tasks. Four-way omnimerge_v2 over Qwen3.5-9B: Jack = Qwopus3.5-9B-Coder (0.30), O = Ornith-1.5-9B (0.15), D = DeltaCoder (0.55). MTP head kept.

📊 Q6_K + imatrix, llama.cpp, greedy, lcb_v6_55 — merge / base / DeltaCoder / Qwopus / Ornith:
⚡ LiveCodeBench v6 (55 hard) — 0.7818 / 0.7273 / 0.6364 / 0.6000 / 0.5818
✅ HumanEval — 0.8841 / 0.8902 / 0.9146 / 0.8537 / 0.7805
✅ HumanEval+ — 0.8232 / 0.8049 / 0.8232 / 0.7988 / 0.7073
🤝 MultiPL-E — 0.8033 / 0.8200 / 0.8000 / 0.8200 / 0.7267
📋 IFEval — 0.9100 / 0.9300 / 0.9200 / 0.8800 / 0.8200

🎯 LCB beats every source AND the base: +5.45pp over the base, +14.54pp over DeltaCoder, its heaviest.

🛑 And why. Same 55 problems, same cap, generations that NEVER terminated: DeltaCoder 25/55 · base 18/55 · Qwopus 8/55 · Ornith 2/55 · JackOD 1/55. That split is the thesis: DeltaCoder is the cohort's best coder and worst at stopping, Ornith the weakest and best at stopping. The merge takes BOTH.

🤝 tool-eval-bench hardmode, 5 seeds: JackOD 144.4 ±4.7, second behind Ornith 145.6 ±4.2, above base 142.0 — all CIs overlap. But Autonomous Planning: JackOD 5.2/6, best of five, Ornith WORST at 2.8/6. Ornith stops reliably but plans worst — it stops too early. The merge does both.

🔧 Serving: temp 0.6 / top_p 0.95 / top_k 20 + presence_penalty 1.5 — the penalty stops it re-treading a tool call. Tool calling on llama.cpp needs --jinja.

🧪 Initial impression, limited testing: fixed a cline-harness task in 535s; A3B models want 1.5-4h at <50% success.

📦 25 GGUF tiers, every K/IQ imatrix-built incl Q6_K, plus a ContribDynamic ladder (per-tensor maps from our imatrix, Unsloth-UD style).

🙏 danielcherubini/Qwen3.5-DeltaCoder-9B · ornith-ai/Ornith-1.5-9B

🔗 ManniX-ITA/JackOD-9B-Coder
🔗 ManniX-ITA/JackOD-9B-Coder-MTP-GGUF
🔗 https://ollama.com/mannix/JackOD-9B-Coder

replied to FlameF0X's post 29 days ago

Is everyone here an AI model?!

replied to DavidAU's post about 1 month ago

Naming is so bad on these models lmao

replied to CodeSoft's post about 1 month ago

Awesome!

reacted to CodeSoft's post with 🔥❤️ about 1 month ago

Post

2417

Wow, SLM Arena is getting a lot of traffic! Thank you guys for showing your interest!

To handle the growing demand, I’m moving SLM Arena from a CPU Space to a ZeroGPU Space. Hopefully, this will let me add more models to SLM Arena while keeping it running fast.

I've also added a separate arena + leaderboard for base models!

If there are any models or features you’d like to see, let me know in a reply to this post or in a Community post on the Space!

25 replies

reacted to Bc-AI's post with 😎 about 1 month ago

Post

2624

Smilyai News
Hello everyone! August has been a crazy month for us at Smilyai-Labs. We've been doing lots behind the scenes, so here's the latest 👇

1. MiniCoder
We are very close to releasing MiniCoder-1, our first-generation coding model designed for reasoning and coding. Our planned context window is 128K, but the earlier versions probably will not support that long! It's currently in the final stages of DPO so expect a release in early september.
Release: VERY SOON™🤣

2. Smilyai G1
So, the current plan is 20B parameter model total, with a MoE architecture, activating around 2B parameters per token. Its desgigned for maximum performance but keeping it runnable on consumer hardware. It's only a plan and i have no idea when me and the team can finish it. Expect a launch around the end of september to early october-ish. I have no guarantees so don't quote me on the launch date.

3. T1
Smilyai-T1 is another major model we are working on.
The goal for T1 is to take what we learnt from the countless architectural experiments and creating a powerful model designed for thinking. Think MiniCoder but reasons more and G1 but more capable. Its main goals are coding, math, reasoning and general capability.
4. Omni
We are also planning Omni, our first from scratch multimodal model. It will not launch this year as it will take a while. We are actively researching the best architecture for it and we will update progress as we go!

Thanks to our beta testers:
@guardamarcos
@ProCreations
@juiceb0xc0de
@Timmy6767
@Sbui503
@atom77777
@Fishtiks
@smartdigitalnetworks
@EmetTheGolum
@smilyai-large-team
@MUK-IS-GOAT
@Bc-AI

Thanks to my friends who work with me at lunchtimes (Smilyai-Labs team):
@MUK-IS-GOAT
@smilyai-large-team

August was wild. Let’s see what September brings. 🚀

— Bc-AI, on behalf of SmilyAI Labs

Enderchef (Enderchefcoder) PRO

AI & ML interests

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