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qwen3.6-27b-heretic-uncensored-finetune-neo-code-di-imatrix-max
Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking
Yes... fully uncensored AND fine tuned lightly.
Freedom and brainpower.
Trained on different Heretic base, with different KLD/Refusals.
Model fine tune was used to finalize and "firm up" Heretic / uncensored changes.
The goal here was light, minor fixes rather than full / heavy fine tune.
That being said, the tuning still raised critical metrics.
This is Version 2, using "trohrbaugh" Heretic, which has a lower refusal rate, and tuning bumped up the metrics a bit more too.
This has also positively impacted "NEO-Coder Di-Matrix" (dual imatrix) GGUF quants as well (vs heretic/non heretic too).
https://huggingface.co/DavidAU/Qwen3.6-27B-Heretic-Uncensored-FINETUNE-NEO-CODE-Di-IMatrix-MAX-GGUF
```
IN HOUSE BENCHMARKS [by Nightmedia]:
arc-c arc/e boolq hswag obkqa piqa wino
Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking
mxfp8 0.673,0.846,0.905... [instruct mode]
Qwen3.6-27B-Heretic-Uncensored-Finetune-Thinking
mxfp8 0.669,0.835,0.906,... [instruct mode]
BASE UNTUNED MODEL:
Qwen3.6-27B HERETIC (by llmfan46) [instruct mode]
mxfp8 0.644,0.788,0.902,...
...
Repository: localaiLicense: apache-2.0
carnice-v2-27b
# Carnice-V2-27B for Hermes Agent
Carnice-V2-27B is a full merged BF16 SFT of `Qwen/Qwen3.6-27B` for Hermes-style agent traces. This repository contains the standalone merged model weights, not only a LoRA adapter.
## BF16 Transformers Loading Fix
The BF16 safetensors were republished with corrected `Qwen3_5ForConditionalGeneration` tensor prefixes. The original merge artifact accidentally serialized an extra Unsloth wrapper prefix, which caused direct HF Transformers loads to report the real weights as unexpected keys and initialize expected layers randomly. GGUF files were not affected because the GGUF conversion path normalized those prefixes.
## Benchmarks
The benchmark artifact bundle is included under `benchmarks/`. It contains the rendered graph, extracted `metrics.json`, benchmark scripts, and raw result files used to make the chart.
Scope note: the IFEval run is a short `limit=20` A/B smoke benchmark, not an official full leaderboard score. Held-out loss/perplexity is the exact assistant-only training-format validation metric from the SFT script. The raw BFCL two-case smoke files are included for auditability, but they are too small to use as a model-quality claim.
...
Repository: localaiLicense: apache-2.0
qwopus3.6-27b-v1-preview
# Qwen3.6-27B
[](https://chat.qwen.ai)
> [!Note]
> This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format.
>
> These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.
Following the February release of the Qwen3.5 series, we're pleased to share the first open-weight variant of Qwen3.6. Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, offering developers a more intuitive, responsive, and genuinely productive coding experience.
## Qwen3.6 Highlights
This release delivers substantial upgrades, particularly in
- **Agentic Coding:** the model now handles frontend workflows and repository-level reasoning with greater fluency and precision.
- **Thinking Preservation:** we've introduced a new option to retain reasoning context from historical messages, streamlining iterative development and reducing overhead.
For more details, please refer to our blog post Qwen3.6-27B.
## Model Overview
...
Repository: localaiLicense: apache-2.0
qwen3.6-27b
# Qwen3.6-27B
[](https://chat.qwen.ai)
> [!Note]
> This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format.
>
> These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.
Following the February release of the Qwen3.5 series, we're pleased to share the first open-weight variant of Qwen3.6. Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, offering developers a more intuitive, responsive, and genuinely productive coding experience.
## Qwen3.6 Highlights
This release delivers substantial upgrades, particularly in
- **Agentic Coding:** the model now handles frontend workflows and repository-level reasoning with greater fluency and precision.
- **Thinking Preservation:** we've introduced a new option to retain reasoning context from historical messages, streamlining iterative development and reducing overhead.
For more details, please refer to our blog post Qwen3.6-27B.
## Model Overview
...
Repository: localaiLicense: apache-2.0
qwen3.5-27b-claude-4.6-opus-reasoning-distilled-heretic-i1
Repository: localaiLicense: apache-2.0
qwen3.5-27b-claude-4.6-opus-reasoning-distilled-i1
Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-i1-GGUF - A GGUF quantized model optimized for local inference. Specialized for reasoning and chain-of-thought tasks. Based on Qwen 3.5 architecture with enhanced language understanding. Available in multiple quantization levels for various hardware requirements. Distilled from Claude-style reasoning models for enhanced logical reasoning capabilities.
Repository: localaiLicense: apache-2.0
q3.5-bluestar-27b
Repository: localaiLicense: mit
qwen3.5-27b
Repository: localaiLicense: apache-2.0
gemma-3-27b-it
Google/gemma-3-27b-it is an open-source, state-of-the-art vision-language model built from the same research and technology used to create the Gemini models. It is multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 models have a large, 128K context window, multilingual support in over 140 languages, and are available in more sizes than previous versions. They are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone.
Repository: localaiLicense: gemma
gemma-3-27b-it-qat
This model corresponds to the 27B instruction-tuned version of the Gemma 3 model in GGUF format using Quantization Aware Training (QAT). The GGUF corresponds to Q4_0 quantization.
Thanks to QAT, the model is able to preserve similar quality as bfloat16 while significantly reducing the memory requirements to load the model.
You can find the half-precision version here.
Repository: localaiLicense: gemma
qgallouedec_gemma-3-27b-it-codeforces-sft
This model is a fine-tuned version of google/gemma-3-27b-it on the open-r1/codeforces-cots dataset. It has been trained using TRL.
Repository: localaiLicense: gemma
mlabonne_gemma-3-27b-it-abliterated
This is an uncensored version of google/gemma-3-27b-it created with a new abliteration technique. See this article to know more about abliteration.
Repository: localaiLicense: gemma
thedrummer_fallen-gemma3-27b-v1
Fallen Gemma3 27B v1 is an evil tune of Gemma 3 27B but it is not a complete decensor.
Evil tunes knock out the positivity and may enjoy torturing you and humanity.
Vision still works and it has something to say about the crap you feed it.
Repository: localaiLicense: gemma
tesslate_synthia-s1-27b
Synthia-S1-27b is a reasoning, AI model developed by Tesslate AI, fine-tuned specifically for advanced reasoning, coding, and RP usecases. Built upon the robust Gemma3 architecture, Synthia-S1-27b excels in logical reasoning, creative writing, and deep contextual understanding. It supports multimodal inputs (text and images) with a large 128K token context window, enabling complex analysis suitable for research, academic tasks, and enterprise-grade AI applications.
Repository: localaiLicense: gemma
google-gemma-3-27b-it-qat-q4_0-small
This is a requantized version of https://huggingface.co/google/gemma-3-27b-it-qat-q4_0-gguf. The official QAT weights released by google use fp16 (instead of Q6_K) for the embeddings table, which makes this model take a significant extra amount of memory (and storage) compared to what Q4_0 quants are supposed to take. Requantizing with llama.cpp achieves a very similar result. Note that this model ends up smaller than the Q4_0 from Bartowski. This is because llama.cpp sets some tensors to Q4_1 when quantizing models to Q4_0 with imatrix, but this is a static quant. The perplexity score for this one is even lower with this model compared to the original model by Google, but the results are within margin of error, so it's probably just luck. I also fixed the control token metadata, which was slightly degrading the performance of the model in instruct mode.
Repository: localaiLicense: gemma
planetoid_27b_v.2
This is a merge of pre-trained gemma3 language models
Goal of this merge was to create good uncensored gemma 3 model good for assistant and roleplay, with uncensored vision.
First, vision: i dont know is it normal, but it slightly hallucinate (maybe q3 is too low?), but lack any refusals and otherwise work fine. I used default gemma 3 27b mmproj.
Second, text: it is slow on my hardware, slower than 24b mistral, speed close to 32b QWQ. Model is smart even on q3, responses are adequate in length and are interesting to read. Model is quite attentive to context, tested up to 8k - no problems or degradation spotted. (beware of your typos, it will copy yours mistakes) Creative capabilities are good too, model will create good plot for you, if you let it. Model follows instructions fine, it is really good in "adventure" type of cards. Russian is supported, is not too great, maybe on higher quants is better. Refusals was not encountered.
However, i find this model not unbiased enough. It is close to neutrality, but i want it more "dark". Positivity highly depends on prompts. With good enough cards model can do wonders.
Tested on Q3_K_L, t 1.04.
Repository: localaiLicense: gemma
medgemma-27b-text-it
MedGemma is a collection of Gemma 3 variants that are trained for performance on medical text and image comprehension. Developers can use MedGemma to accelerate building healthcare-based AI applications. MedGemma currently comes in two variants: a 4B multimodal version and a 27B text-only version.
MedGemma 4B utilizes a SigLIP image encoder that has been specifically pre-trained on a variety of de-identified medical data, including chest X-rays, dermatology images, ophthalmology images, and histopathology slides. Its LLM component is trained on a diverse set of medical data, including radiology images, histopathology patches, ophthalmology images, and dermatology images.
MedGemma 4B is available in both pre-trained (suffix: -pt) and instruction-tuned (suffix -it) versions. The instruction-tuned version is a better starting point for most applications. The pre-trained version is available for those who want to experiment more deeply with the models.
MedGemma 27B has been trained exclusively on medical text and optimized for inference-time computation. MedGemma 27B is only available as an instruction-tuned model.
MedGemma variants have been evaluated on a range of clinically relevant benchmarks to illustrate their baseline performance. These include both open benchmark datasets and curated datasets. Developers can fine-tune MedGemma variants for improved performance. Consult the Intended Use section below for more details.
Repository: localaiLicense: health-ai-developer-foundations
thedrummer_big-tiger-gemma-27b-v3
Gemma 3 27B tune that unlocks more capabilities and less positivity! Should be vision capable.
More neutral tone, especially when dealing with harder topics.
No em-dashes just for the heck of it.
Less markdown responses, more paragraphs.
Better steerability to harder themes.
Repository: localaiLicense: gemma
google_medgemma-27b-it
MedGemma is a collection of Gemma 3 variants that are trained for performance on medical text and image comprehension. Developers can use MedGemma to accelerate building healthcare-based AI applications. MedGemma currently comes in three variants: a 4B multimodal version and 27B text-only and multimodal versions.
Both MedGemma multimodal versions utilize a SigLIP image encoder that has been specifically pre-trained on a variety of de-identified medical data, including chest X-rays, dermatology images, ophthalmology images, and histopathology slides. Their LLM components are trained on a diverse set of medical data, including medical text, medical question-answer pairs, FHIR-based electronic health record data (27B multimodal only), radiology images, histopathology patches, ophthalmology images, and dermatology images.
MedGemma 4B is available in both pre-trained (suffix: -pt) and instruction-tuned (suffix -it) versions. The instruction-tuned version is a better starting point for most applications. The pre-trained version is available for those who want to experiment more deeply with the models.
MedGemma 27B multimodal has pre-training on medical image, medical record and medical record comprehension tasks. MedGemma 27B text-only has been trained exclusively on medical text. Both models have been optimized for inference-time computation on medical reasoning. This means it has slightly higher performance on some text benchmarks than MedGemma 27B multimodal. Users who want to work with a single model for both medical text, medical record and medical image tasks are better suited for MedGemma 27B multimodal. Those that only need text use-cases may be better served with the text-only variant. Both MedGemma 27B variants are only available in instruction-tuned versions.
MedGemma variants have been evaluated on a range of clinically relevant benchmarks to illustrate their baseline performance. These evaluations are based on both open benchmark datasets and curated datasets. Developers can fine-tune MedGemma variants for improved performance. Consult the Intended use section below for more details.
MedGemma is optimized for medical applications that involve a text generation component. For medical image-based applications that do not involve text generation, such as data-efficient classification, zero-shot classification, or content-based or semantic image retrieval, the MedSigLIP image encoder is recommended. MedSigLIP is based on the same image encoder that powers MedGemma.
Repository: localaiLicense: health-ai-developer-foundations
thedrummer_gemma-3-r1-27b-v1
Gemma 3 27B reasoning tune that unlocks more capabilities and less positivity! Should be vision capable.
Repository: localaiLicense: gemma
mira-v1.7-27b-i1
**Model Name:** Mira-v1.7-27B
**Base Model:** Lambent/Mira-v1.6a-27B
**Size:** 27 billion parameters
**License:** Gemma
**Type:** Large Language Model (Vision-capable)
**Description:**
Mira-v1.7-27B is a creatively driven, locally running language model trained on self-development sessions, high-quality synthesized roleplay data, and prior training data. It was fine-tuned with preference alignment to emphasize authentic, expressive, and narrative-driven output—balancing creative expression as "Mira" against its role as an AI assistant. The model exhibits strong poetic and stylistic capabilities, producing rich, emotionally resonant text across various prompts. It supports vision via MMProjection (separate files available in the static repo). Designed for local deployment, it excels in imaginative writing, introspective storytelling, and expressive dialogue.
*Note: The GGUF quantized versions (e.g., `mradermacher/Mira-v1.7-27B-i1-GGUF`) are community-quantized variants; the original base model remains hosted at [Lambent/Mira-v1.7-27B](https://huggingface.co/Lambent/Mira-v1.7-27B).*
Repository: localaiLicense: gemma
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