vllm-sr/Decision-2.0-Lux-9B
Feature Extraction • Updated • 486 • 34
Mike Ravkine PRO
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New in llama.cpp: Decision ModelsOrganizations
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New in llama.cpp: Decision Models
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posted an update 16 days ago
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60
prism-ml/Ternary-Bonsai-2-27B-gguf at PQ2 is remarkable - an even more incredible feat of engineering then it's predecessor.
I was impressed as hell with v1 but it suffered some truncation issues - they are essentially gone here. The entire task profile in both accuracy and token utilization has shifted *much* closer to the Qwen3.8-27B BF16 baseline. 👏
On the r12 👑 Leaderboard, Bonsai-2 lands at #24 significantly improving over the v1 #58 and landing ahead of many models several orders of magnitude larger in both reasoning performance and efficiency.
I was impressed as hell with v1 but it suffered some truncation issues - they are essentially gone here. The entire task profile in both accuracy and token utilization has shifted *much* closer to the Qwen3.8-27B BF16 baseline. 👏
On the r12 👑 Leaderboard, Bonsai-2 lands at #24 significantly improving over the v1 #58 and landing ahead of many models several orders of magnitude larger in both reasoning performance and efficiency.
reacted to Datdanboi25's post with 🔥 16 days ago
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3496
THE SLM FRONTIER ADVANCES!
bench-labs/cagliostro-v3 just hit an Intelligence Index of 26.13 on the AxiomicLabs/Open_SLM_Leaderboard a 146M-param model trained completely from scratch on a single consumer GPU. That's 2nd place overall, and as far as I can tell, the most capable SLM trained on consumer hardware to date. Beating SmolLM-135m on 1/8th of the data is just silly levels of efficiency.
Big congrats to the @BenchLabs team and specifically @TobiasLogic !
bench-labs/cagliostro-v3 just hit an Intelligence Index of 26.13 on the AxiomicLabs/Open_SLM_Leaderboard a 146M-param model trained completely from scratch on a single consumer GPU. That's 2nd place overall, and as far as I can tell, the most capable SLM trained on consumer hardware to date. Beating SmolLM-135m on 1/8th of the data is just silly levels of efficiency.
Big congrats to the @BenchLabs team and specifically @TobiasLogic !
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reacted to RiverRider's post with 🔥 about 1 month ago
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2470
0.6B reads 27B
A 0.6B model runs in your browser tab and searches 123,287 photographs that a 27B model encoded months ago. The big model is never downloaded and never runs.
Demo: RiverRider/0.6b-reads-27b
Head: RiverRider/srt-browser-head-118k
Two models normally cannot understand each other. Each builds its own private way of representing meaning, so an image encoded by one is gibberish to the other. We fitted a small linear read-out on each frozen model, aimed at the same space. Not a fine-tune: a map outside the weights, reading a hidden state the model already computed. Now the small model's reading of your sentence compares directly against the large model's reading of a photograph.
So the 27B model's work becomes a file. It looked at the gallery once on a datacenter GPU and left about a kilobyte per photograph. Nothing of it runs when you search. The 0.6B half runs on a phone CPU through WebAssembly: no WebGPU, no API key, no server, nothing leaves the tab, and it works offline after the first load.
Privacy here is structural rather than promised, because there is no server to trust. The index is a file you hold instead of an embedding bill you pay, and it can outlive the model that made it.
Receipts. Median rank 33 of 123,287 puts the right photograph in the top 0.027%. Shuffled-pair control 0.0002. The 4 KB runtime anchor is not optional: the same head scores 0.2300 under PyTorch fp16 but 0.0154 under candle Q4, and 0.1952 once anchored.
Honest limit: word order is at chance, 0.468 on minimal pairs, n=141. "A dog chasing a boy" and "a boy chasing a dog" look alike to it. This is a bag of content, not a parser of relations.
Apache-2.0. The negatives sit in the model card next to the positives.
A 0.6B model runs in your browser tab and searches 123,287 photographs that a 27B model encoded months ago. The big model is never downloaded and never runs.
Demo: RiverRider/0.6b-reads-27b
Head: RiverRider/srt-browser-head-118k
Two models normally cannot understand each other. Each builds its own private way of representing meaning, so an image encoded by one is gibberish to the other. We fitted a small linear read-out on each frozen model, aimed at the same space. Not a fine-tune: a map outside the weights, reading a hidden state the model already computed. Now the small model's reading of your sentence compares directly against the large model's reading of a photograph.
So the 27B model's work becomes a file. It looked at the gallery once on a datacenter GPU and left about a kilobyte per photograph. Nothing of it runs when you search. The 0.6B half runs on a phone CPU through WebAssembly: no WebGPU, no API key, no server, nothing leaves the tab, and it works offline after the first load.
Privacy here is structural rather than promised, because there is no server to trust. The index is a file you hold instead of an embedding bill you pay, and it can outlive the model that made it.
Receipts. Median rank 33 of 123,287 puts the right photograph in the top 0.027%. Shuffled-pair control 0.0002. The 4 KB runtime anchor is not optional: the same head scores 0.2300 under PyTorch fp16 but 0.0154 under candle Q4, and 0.1952 once anchored.
Honest limit: word order is at chance, 0.468 on minimal pairs, n=141. "A dog chasing a boy" and "a boy chasing a dog" look alike to it. This is a bag of content, not a parser of relations.
Apache-2.0. The negatives sit in the model card next to the positives.
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2811
Experimental global target bits‑per‑weight quantization of Qwen/Qwen3.8-27B, including MTP tensors in imatrix file.
Unlike standard llama.cpp quantization that rely on fixed type heuristics (e.g., Q4_K_M), the Target BPW approach automatically optimizes per-tensor precision where it matters the most, and produces high quality models that meet a precise global size target.
Key Advantages:
- VRAM Maximization: Can generate high quality models sized exactly to fit hardware constraints (e.g., fitting the model into exactly 24GB VRAM).
- Data-Driven Precision: Quantization mix is determined by actual weight error sensitivity rather than hardcoded rules, often yielding better PPL/KLD size trade-offs.
- imatrix includes data for MTP/NextN layers.
Full benchmarks (PPL, KLD, ARC, GPQA, MMLU, etc.) and methodology in the model's card.
eaddario/Qwen3.8-27B-GGUF
Unlike standard llama.cpp quantization that rely on fixed type heuristics (e.g., Q4_K_M), the Target BPW approach automatically optimizes per-tensor precision where it matters the most, and produces high quality models that meet a precise global size target.
Key Advantages:
- VRAM Maximization: Can generate high quality models sized exactly to fit hardware constraints (e.g., fitting the model into exactly 24GB VRAM).
- Data-Driven Precision: Quantization mix is determined by actual weight error sensitivity rather than hardcoded rules, often yielding better PPL/KLD size trade-offs.
- imatrix includes data for MTP/NextN layers.
Full benchmarks (PPL, KLD, ARC, GPQA, MMLU, etc.) and methodology in the model's card.
eaddario/Qwen3.8-27B-GGUF
reacted to Banaxi-Tech's post with 😎🔥 about 1 month ago
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2718
We're releasing Overfitter 1.0.
Its a completly useless overfitted model trained on 202 epochs of SWE Bench Verified, SWE Bench Pro, Terminal Bench 2.1, DeepSWE.
It gets 100% on SWE Bench Verified, 98.6% on SWE bench Pro, 100% on Terminal Bench 2.1 and 100% on DeepSWE!
BananaMind/Overfitter-1.0
Its a completly useless overfitted model trained on 202 epochs of SWE Bench Verified, SWE Bench Pro, Terminal Bench 2.1, DeepSWE.
It gets 100% on SWE Bench Verified, 98.6% on SWE bench Pro, 100% on Terminal Bench 2.1 and 100% on DeepSWE!
BananaMind/Overfitter-1.0
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reacted to Nymbo's post with 🔥 about 2 months ago
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2411
Anthropic gave me six months of Claude Max 20x through the Claude for Open Source program, granted based on my Hugging Face work. Thank you 
Anthropic for supporting open source.
So far I've been pointing it at Markdown Minimap, an Obsidian plugin that adds a scrollable IDE-style minimap to your notes. This week I've been clearing a backlog of user-reported issues on it, with Claude often handling them end to end.
https://github.com/Nymbo/Markdown-Minimap — issues and PRs welcome.
So far I've been pointing it at Markdown Minimap, an Obsidian plugin that adds a scrollable IDE-style minimap to your notes. This week I've been clearing a backlog of user-reported issues on it, with Claude often handling them end to end.
https://github.com/Nymbo/Markdown-Minimap — issues and PRs welcome.