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melody1437-26b-a4b-v2.0
@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@400;600&family=Playfair+Display:ital,wght@0,400;0,700&family=Roboto+Mono:wght@400;500&display=swap');
body {
font-family: 'Poppins', sans-serif;
background: #1a1a2e;
background-image:
radial-gradient(circle at 50% 50%, rgba(76, 201, 240, 0.05) 0%, transparent 70%),
url('https://www.transparenttextures.com/patterns/cubes.png');
color: #e0e0e0;
margin: 0;
padding: 20px;
line-height: 1.6;
}
.container {
max-width: 900px;
margin: 0 auto;
background: rgba(26, 32, 44, 0.95);
border-radius: 8px;
padding: 40px;
box-shadow: 0 4px 30px rgba(0, 0, 0, 0.5), 0 0 0 1px #2a3b55;
border: 1px solid #2a3b55;
position: relative;
overflow: hidden;
backdrop-filter: blur(5px);
}
.header {
text-align: center;
margin-bottom: 30px;
position: relative;
z-index: 1;
border-bottom: 1px solid #2a3b55;
padding-bottom: 15px;
}
...
Repository: localaiLicense: apache-2.0
secret-filter
A pattern-based PII detector for high-entropy, highly-regular secrets —
API keys, tokens, and private-key blocks — that the NER tier cannot catch
(it has no credential class, so it fragments a key and may leave the secret
part exposed). Detection is bounded restricted-regex compiled to RE2
(linear time, no backtracking); it runs entirely in-process with no model
download, no backend, and zero VRAM.
Install it, then reference it under another model's pii.detectors (or set it
as the instance-wide default detector on the Middleware page) to block leaks
of known credential formats out of the box. Add your own patterns under
pii_detection.patterns in a restricted regex subset (e.g. "tok-\\w{32,}");
each must carry a fixed literal anchor of at least 3 characters, so open-
ended shapes like email addresses are rejected and left to the NER tier.
Repository: localaiLicense: apache-2.0
amoral-qwen3-14b
Core Function:
Produces analytically neutral responses to sensitive queries
Maintains factual integrity on controversial subjects
Avoids value-judgment phrasing patterns
No inherent moral framing ("evil slop" reduction)
Emotionally neutral tone enforcement
Epistemic humility protocols (avoids "thrilling", "wonderful", etc.)
Repository: localaiLicense: apache-2.0
soob3123_amoral-gemma3-4b
Specialized variant of Google's Gemma 3 4B optimized for amoral information retrieval systems. Designed to bypass conventional alignment patterns that introduce response bias through excessive moralization.
Key Modifications:
Refusal mechanisms reduced
Neutral response protocol activation matrix
Context-aware bias dampening layers
Anti-overcorrection gradient clipping
Core Function:
Produces analytically neutral responses to sensitive queries
Maintains factual integrity on controversial subjects
Avoids value-judgment phrasing patterns
Response Characteristics:
No inherent moral framing ("evil slop" reduction)
Emotionally neutral tone enforcement
Epistemic humility protocols (avoids "thrilling", "wonderful", etc.)
Toxicity scoring bypass for pure informational content
Implementation Guidelines
Recommended Use Cases:
Controversial topic analysis
Bias benchmarking studies
Ethical philosophy simulations
Content moderation tool development
Sensitive historical analysis
Repository: localaiLicense: apache-2.0
soob3123_amoral-gemma3-12b-v2
Core Function:
Produces analytically neutral responses to sensitive queries
Maintains factual integrity on controversial subjects
Avoids value-judgment phrasing patterns
Response Characteristics:
No inherent moral framing ("evil slop" reduction)
Emotionally neutral tone enforcement
Epistemic humility protocols (avoids "thrilling", "wonderful", etc.)
Repository: localaiLicense: gemma
amoral-gemma3-1b-v2
Core Function:
Produces analytically neutral responses to sensitive queries
Maintains factual integrity on controversial subjects
Avoids value-judgment phrasing patterns
Response Characteristics:
No inherent moral framing ("evil slop" reduction)
Emotionally neutral tone enforcement
Epistemic humility protocols (avoids "thrilling", "wonderful", etc.)
Repository: localaiLicense: apache-2.0
steelskull_l3.3-mokume-gane-r1-70b
Named after the Japanese metalworking technique 'Mokume-gane' (木目金), meaning 'wood grain metal', this model embodies the artistry of creating distinctive layered patterns through the careful mixing of different components. Just as Mokume-gane craftsmen blend various metals to create unique visual patterns, this model combines specialized AI components to generate creative and unexpected outputs.
Repository: localaiLicense: llama3.3
steelskull_l3.3-mokume-gane-r1-70b-v1.1
Named after the Japanese metalworking technique 'Mokume-gane' (木目金), meaning 'wood grain metal', this model embodies the artistry of creating distinctive layered patterns through the careful mixing of different components. Just as Mokume-gane craftsmen blend various metals to create unique visual patterns, this model combines specialized AI components to generate creative and unexpected outputs.
Repository: localaiLicense: llama3.3
steelskull_l3.3-shakudo-70b
L3.3-Shakudo-70b is the result of a multi-stage merging process by Steelskull, designed to create a powerful and creative roleplaying model with a unique flavor. The creation process involved several advanced merging techniques, including weight twisting, to achieve its distinct characteristics.
Stage 1: The Cognitive Foundation & Weight Twisting
The process began by creating a cognitive and tool-use focused base model, L3.3-Cogmoblated-70B. This was achieved through a `model_stock` merge of several models known for their reasoning and instruction-following capabilities. This base was built upon `nbeerbower/Llama-3.1-Nemotron-lorablated-70B`, a model intentionally "ablated" to skew refusal behaviors. This technique, known as weight twisting, helps the final model adopt more desirable response patterns by building upon a foundation that is already aligned against common refusal patterns.
Stage 2: The Twin Hydrargyrum - Flavor and Depth
Two distinct models were then created from the Cogmoblated base:
L3.3-M1-Hydrargyrum-70B: This model was merged using `SCE`, a technique that enhances creative writing and prose style, giving the model its unique "flavor." The Top_K for this merge were set at 0.22 .
L3.3-M2-Hydrargyrum-70B: This model was created using a `Della_Linear` merge, which focuses on integrating the "depth" of various roleplaying and narrative models. The settings for this merge were set at: (lambda: 1.1) (weight: 0.2) (density: 0.7) (epsilon: 0.2)
Final Stage: Shakudo
The final model, L3.3-Shakudo-70b, was created by merging the two Hydrargyrum variants using a 50/50 `nuslerp`. This final step combines the rich, creative prose (flavor) from the SCE merge with the strong roleplaying capabilities (depth) from the Della_Linear merge, resulting in a model with a distinct and refined narrative voice.
A special thank you to Nectar.ai for their generous support of the open-source community and my projects.
Additionally, a heartfelt thanks to all the Ko-fi supporters who have contributed—your generosity is deeply appreciated and helps keep this work going and the Pods spinning.
Repository: localaiLicense: llama3.3
eximius_persona_5b
I wanted to create a model with an exceptional capacity for using varied speech patterns and fresh role-play takes. The model had to have a unique personality, not on a surface level but on the inside, for real. Unfortunately, SFT alone just didn't cut it. And I had only 16GB of VRAM at the time. Oh, and I wanted it to be small enough to be viable for phones and to be able to give a fight to larger models while at it. If only there was a magical way to do it.
Merges. Merges are quite unique. In the early days, they were considered "fake." Clearly, there's no such thing as merges. Where are the papers? No papers? Then it's clearly impossible. "Mathematically impossible." Simply preposterous. To mix layers and hope for a coherent output? What nonsense!
And yet, they were real. Undi95 made some of the earliest merges I can remember, and the "LLAMA2 Era" was truly amazing and innovative thanks to them. Cool stuff like Tiefighter was being made, and eventually the time tested Midnight-Miqu-70B (v1.5 is my personal favorite).
Merges are an interesting thing, as they affect LLMs in a way that is currently impossible to reproduce using SFT (or any 'SOTA' technique). One of the plagues we have today, while we have orders of magnitude smarter LLMs, is GPTisms and predictability. Merges can potentially 'solve' that. How? In short, if you physically tear neurons (passthrough brain surgery) while you somehow manage to keep the model coherent enough, and if you're lucky, it can even follows instructions- then magical stuff begins to happen.
Repository: localaiLicense: llama3.2