Qwen/Qwen-Image-Bench
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Alex Hant
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reacted to danielhanchen's post with 👍 6 days ago
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2306
Qwen3.6 MTP is here! Run locally on 20GB RAM. ⚡️
MTP enables Qwen3.6 to generate ~1.4–2.2× faster with no accuracy change.
Qwen3.6-27B: unsloth/Qwen3.6-27B-MTP-GGUF
Qwen3.6-35B-A3B: unsloth/Qwen3.6-35B-A3B-MTP-GGUF
Guide: https://unsloth.ai/docs/models/qwen3.6#mtp-guide
MTP enables Qwen3.6 to generate ~1.4–2.2× faster with no accuracy change.
Qwen3.6-27B: unsloth/Qwen3.6-27B-MTP-GGUF
Qwen3.6-35B-A3B: unsloth/Qwen3.6-35B-A3B-MTP-GGUF
Guide: https://unsloth.ai/docs/models/qwen3.6#mtp-guide
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reacted to Crownelius's post with 🔥 7 days ago
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4574
Howdy,
CompactAI-O is launching a tiny Model Golf, and the winner walks away with $50 in RunPod credits. Monthly. Every month. Show up, build, somebody wins.
What it is
Build the best language model you can under 100 million parameters, with at least a 1028-token context window. That's it. Any architecture, any tokenizer, any training scheme you can dream up at 3am. The only catch is it's gotta be open source (MIT, GPL, Apache, AGPL) take your pick.
It scratches the same itch as a Kaggle comp without the dataset\leaderboard nonsense. No fixed benchmark to game. No llama.cpp compatibility hoops. If you wanna train a 50M-param MoE with five experts and a tokenizer built on cookbooks, you can do that. Nothing stopping you.
The rules are listed in the discord and on the organization page if you're interested.
Why $50????
It's symbolic. It ain't gonna make anyone rich. But it's enough to cover a weekend of GPU time, enough to keep enthusiasts coming back, and not so much that it pulls in people who are just there for the money. Enthusiasts build interesting things. Interesting things move the field forward. A little incentive. I'd do it for $50 lol.
How to join
First round opens soon. Landing page is here:
→ CompactAI-O/Tiny-model-golf
For questions or to swap ideas, the Discord's open:
→ https://discord.gg/y2jTct6Cxv
Excited to see what yall come up with. ♥
— Shane
CompactAI-O is launching a tiny Model Golf, and the winner walks away with $50 in RunPod credits. Monthly. Every month. Show up, build, somebody wins.
What it is
Build the best language model you can under 100 million parameters, with at least a 1028-token context window. That's it. Any architecture, any tokenizer, any training scheme you can dream up at 3am. The only catch is it's gotta be open source (MIT, GPL, Apache, AGPL) take your pick.
It scratches the same itch as a Kaggle comp without the dataset\leaderboard nonsense. No fixed benchmark to game. No llama.cpp compatibility hoops. If you wanna train a 50M-param MoE with five experts and a tokenizer built on cookbooks, you can do that. Nothing stopping you.
The rules are listed in the discord and on the organization page if you're interested.
Why $50????
It's symbolic. It ain't gonna make anyone rich. But it's enough to cover a weekend of GPU time, enough to keep enthusiasts coming back, and not so much that it pulls in people who are just there for the money. Enthusiasts build interesting things. Interesting things move the field forward. A little incentive. I'd do it for $50 lol.
How to join
First round opens soon. Landing page is here:
→ CompactAI-O/Tiny-model-golf
For questions or to swap ideas, the Discord's open:
→ https://discord.gg/y2jTct6Cxv
Excited to see what yall come up with. ♥
— Shane
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reacted to danielhanchen's post with 🔥 15 days ago
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5768
We’re excited to announce that Unsloth has joined the PyTorch Ecosystem! 🔥🦥
Unsloth is an open-source project that makes training & running models more accurate and faster with less compute. Our mission is to make local AI accessible to everyone. Thanks to all of you for making this possible! 💕
Blog: https://unsloth.ai/blog/pytorch
GitHub: https://github.com/unslothai/unsloth
Unsloth is an open-source project that makes training & running models more accurate and faster with less compute. Our mission is to make local AI accessible to everyone. Thanks to all of you for making this possible! 💕
Blog: https://unsloth.ai/blog/pytorch
GitHub: https://github.com/unslothai/unsloth
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reacted to DedeProGames's post with 🔥 20 days ago
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5081
🚀 Introducing the GRM-2.6 Family
The GRM-2.6 family is a new generation of reasoning-focused models from Orion LLM Labs, built for difficult tasks, coding, STEM, terminal agents, and advanced local AI workflows.
GRM-2.6-Plus is the main high-capability model in the family: a 27B-class reasoning model based on Qwen3.6, designed for strong structured reasoning, coding, agentic use, and practical local deployment.
GRM-2.6-Opus builds on GRM-2.6-Plus as a merge with an Opus-style reasoning distilled model, improving structured reasoning behavior, terminal-agent workflows, coding ability, and complex problem solving.
Both models are designed for users who want powerful reasoning models that remain practical for research, local inference, coding, and agent experiments.
Models:
GRM-2.6-Plus: OrionLLM/GRM-2.6-Plus
GRM-2.6-Opus: OrionLLM/GRM-2.6-Opus
Organization:
OrionLLM
The GRM-2.6 family is a new generation of reasoning-focused models from Orion LLM Labs, built for difficult tasks, coding, STEM, terminal agents, and advanced local AI workflows.
GRM-2.6-Plus is the main high-capability model in the family: a 27B-class reasoning model based on Qwen3.6, designed for strong structured reasoning, coding, agentic use, and practical local deployment.
GRM-2.6-Opus builds on GRM-2.6-Plus as a merge with an Opus-style reasoning distilled model, improving structured reasoning behavior, terminal-agent workflows, coding ability, and complex problem solving.
Both models are designed for users who want powerful reasoning models that remain practical for research, local inference, coding, and agent experiments.
Models:
GRM-2.6-Plus: OrionLLM/GRM-2.6-Plus
GRM-2.6-Opus: OrionLLM/GRM-2.6-Opus
Organization:
- 2 replies
reacted to danielhanchen's post with 🔥 23 days ago
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8818
We made a guide on how to run open LLMs in Claude Code, Codex and OpenClaw.
Use Gemma 4 and Qwen3.6 GGUFs for local agentic coding on 24GB RAM
Run with self-healing tool calls, code execution, web search via the Unsloth API endpoint and llama.cpp
Guide: https://unsloth.ai/docs/basics/api
Use Gemma 4 and Qwen3.6 GGUFs for local agentic coding on 24GB RAM
Run with self-healing tool calls, code execution, web search via the Unsloth API endpoint and llama.cpp
Guide: https://unsloth.ai/docs/basics/api
reacted to DedeProGames's post with 🔥 23 days ago
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8577
GRaPE 2 Pro is now available.
SL-AI/GRaPE-2-Pro
This is the flagship model of the GRaPE 2 family and the largest model I have trained to date, sitting at 27B parameters. It is built on Qwen3.5-27B and trained on a closed-source proprietary dataset, with roughly half of post-training focused on code and the rest split between STEAM subjects and structured logical reasoning. It punches seriously above its weight class.
GRaPE 2 Pro supports multimodal input (image + text) and features 6 thinking modes via the
It also runs on consumer hardware. You can get it going with as low as 12GB of VRAM on a quantized build.
If you want to try it out and give feedback, that would be really appreciated. Email us at
SL-AI/GRaPE-2-Pro
This is the flagship model of the GRaPE 2 family and the largest model I have trained to date, sitting at 27B parameters. It is built on Qwen3.5-27B and trained on a closed-source proprietary dataset, with roughly half of post-training focused on code and the rest split between STEAM subjects and structured logical reasoning. It punches seriously above its weight class.
GRaPE 2 Pro supports multimodal input (image + text) and features 6 thinking modes via the
minimal, all the way up to xtra-Hi for deep, extended thought on hard problems. For most agentic use, auto or low is the move to keep things snappy.It also runs on consumer hardware. You can get it going with as low as 12GB of VRAM on a quantized build.
If you want to try it out and give feedback, that would be really appreciated. Email us at
contact@skinnertopia.com 
- 1 reply
reacted to ManniX-ITA's post with 🚀 27 days ago
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3019
🚀 Two releases this week pushing merge methodology forward.
▶ Qwen3.6-27B-Omnimerge-v4-MLP
ManniX-ITA/Qwen3.6-27B-Omnimerge-v4
Same-base DARE-TIES merge of Qwen3.6-27B + 3 fine-tunes (rico03 Claude distill, Esper3.1, kai-os Opus reasoning anchor) via my Omnimerge_v2 method (OBIM-lite + DAREx-q + EMR election).
Hit a Qwen3.6-specific fragility: hyperparams that work flawlessly on 3.5 produced 80% unclosed-<think> on 3.6, collapsing pass@1 to ~20%. Per-tensor delta forensics localized the failure to mlp.{gate,up,down}_proj in
layers 27–52. Fix: MLP-passthrough surgery — copy MLPs verbatim from base, keep merged attn + linear_attn. Leak → 0%.
Q6_K results (vs Qwen3.6 base / vs Omnimerge-v2 on Qwen3.5):
• HumanEval: 84.76% (= base, +5.49 pp vs v2)
• MBPP corrected: 73.40% (+15.80 pp vs base, ≈ v2)
• GPQA Diamond: ~84.75% partial 192/198 (+15.5 pp vs v2)
▶ Qwen3.5-4B Importance-Signal Study (M1..M5)
Controlled 5-way comparison: same Qwen3.5-4B base, same 2 fine-tunes (Jackrong Claude-4.5 distill + Crow Opus-4.6 distill), only the importance signal driving DARE-TIES sparsification varies.
Q6_K HE / MBPP pass@1:
• M1 Vanilla DARE-TIES → 51.22 / 47.00
• M2 OMv2 (no signal) → 52.44 / 49.40
• M3 OMv2 + Fisher → 57.93 🥇 / 48.80
• M4 mergekit ex-LRP (PR #682) → 51.22 / 49.40
• M5 OMv2 + LRP → 53.05 / 51.40 🥇
Findings: Fisher wins HE (+4.88 pp over vanilla), LRP wins MBPP (+2.60 pp). Both signals + Omnimerge_v2 recipe beat vanilla. To make multimodal-LM ex-LRP work end-to-end against Qwen3_5ForConditionalGeneration, I filed
5 patches against arcee-ai/mergekit PR #682 + 1 against rachtibat/lxt.
All five Mx checkpoints + Fisher/LRP signal safetensors + reproducer scripts published.
▶ Qwen3.6-27B-Omnimerge-v4-MLP
ManniX-ITA/Qwen3.6-27B-Omnimerge-v4
Same-base DARE-TIES merge of Qwen3.6-27B + 3 fine-tunes (rico03 Claude distill, Esper3.1, kai-os Opus reasoning anchor) via my Omnimerge_v2 method (OBIM-lite + DAREx-q + EMR election).
Hit a Qwen3.6-specific fragility: hyperparams that work flawlessly on 3.5 produced 80% unclosed-<think> on 3.6, collapsing pass@1 to ~20%. Per-tensor delta forensics localized the failure to mlp.{gate,up,down}_proj in
layers 27–52. Fix: MLP-passthrough surgery — copy MLPs verbatim from base, keep merged attn + linear_attn. Leak → 0%.
Q6_K results (vs Qwen3.6 base / vs Omnimerge-v2 on Qwen3.5):
• HumanEval: 84.76% (= base, +5.49 pp vs v2)
• MBPP corrected: 73.40% (+15.80 pp vs base, ≈ v2)
• GPQA Diamond: ~84.75% partial 192/198 (+15.5 pp vs v2)
▶ Qwen3.5-4B Importance-Signal Study (M1..M5)
Controlled 5-way comparison: same Qwen3.5-4B base, same 2 fine-tunes (Jackrong Claude-4.5 distill + Crow Opus-4.6 distill), only the importance signal driving DARE-TIES sparsification varies.
Q6_K HE / MBPP pass@1:
• M1 Vanilla DARE-TIES → 51.22 / 47.00
• M2 OMv2 (no signal) → 52.44 / 49.40
• M3 OMv2 + Fisher → 57.93 🥇 / 48.80
• M4 mergekit ex-LRP (PR #682) → 51.22 / 49.40
• M5 OMv2 + LRP → 53.05 / 51.40 🥇
Findings: Fisher wins HE (+4.88 pp over vanilla), LRP wins MBPP (+2.60 pp). Both signals + Omnimerge_v2 recipe beat vanilla. To make multimodal-LM ex-LRP work end-to-end against Qwen3_5ForConditionalGeneration, I filed
5 patches against arcee-ai/mergekit PR #682 + 1 against rachtibat/lxt.
All five Mx checkpoints + Fisher/LRP signal safetensors + reproducer scripts published.
- 1 reply
reacted to danielhanchen's post with 🤗 29 days ago
reacted to ajibawa-2023's post with 🚀 about 1 month ago
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6114
Go-Code-Large
Dataset: ajibawa-2023/Go-Code-Large
Go-Code-Large is a large-scale corpus of Go (Golang) programming language source code, comprising 316,427 code samples stored in .jsonl format. The dataset is designed to support research and development in large language model (LLM) pretraining, static analysis, cloud-native systems, and modern backend software engineering.
By offering a focused and curated dataset for Go, this corpus enables experimentation in concurrent programming, distributed systems, and performance-oriented backend services—domains where Go is widely adopted.
Go-Code-Large addresses the relative scarcity of large, language-specific datasets for Go, enabling targeted research into idiomatic Go patterns, concurrency primitives, and scalable system design.
Dataset: ajibawa-2023/Go-Code-Large
Go-Code-Large is a large-scale corpus of Go (Golang) programming language source code, comprising 316,427 code samples stored in .jsonl format. The dataset is designed to support research and development in large language model (LLM) pretraining, static analysis, cloud-native systems, and modern backend software engineering.
By offering a focused and curated dataset for Go, this corpus enables experimentation in concurrent programming, distributed systems, and performance-oriented backend services—domains where Go is widely adopted.
Go-Code-Large addresses the relative scarcity of large, language-specific datasets for Go, enabling targeted research into idiomatic Go patterns, concurrency primitives, and scalable system design.
- 2 replies