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glm-5.3-flash
# GLM-5.3-Flash
👋 Join our WeChat or Discord community.
📖 Check out the GLM-5.3-Flash blog and GLM-5 Technical report.
📍 Use GLM-5.3-Flash API services on Z.ai API Platform.
## Introduction
We introduce GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks.
GLM-5.3-Flash starts from a newly trained base model, with its architecture and training recipe redesigned around capability and efficiency. For the first time in the GLM series, we introduce a hybrid architecture combining sparse and linear attention, sharply reducing long-context serving costs while preserving precise long-context capabilities. The model also adopts Manifold-Constrained Hyper-Connections (mHC) to further improve scaling efficiency. Together with our latest 30T-token multimodal pre-training corpus, these changes enable GLM-5.3-Flash to deliver more intelligence with less compute.
## Serve GLM-5.3-Flash Locally
...
Repository: localaiLicense: mit
glm-5.3
# GLM-5.3
GLM-5.3 uses the same base model as GLM-5.2 — every gain comes from post-training. Compared with GLM-5.2, it is much better at complex coding and long-horizon tasks:
+ Stronger Coding: GLM-5.3 is the most capable open-weights model for coding, with a 50% improvement over GLM-5.2 on our in-house Z.ai Code Bench. It also achieve open-source SOTA on public benchmarks including Terminal Bench 3.0 and Agents' Last Exam.
+ Emergent Cyber Capability: As we scaled post-training, cyber capability developed faster than we expected. GLM-5.3 is state of the art on CyberGym for vulnerability discovery, and its gains are largest further up the exploitation chain, where it more than doubles GLM-5.2 on exploitation benchmarks.
## Benchmark
### Serve GLM-5.3 Locally
GLM-5.3 supports deployment with the following frameworks. Feel free to try them out:
- SGLang — see cookbook
- vLLM — see recipes
- TokenSpeed — see here
- Transformers — see transformers docs
- KTransformers — see tutorial
- Unsloth — see guide
- For deployment on the `Ascend NPU` platform, inference frameworks such as vLLM-Ascend, xLLM and SGLang are supported — see here.
### Note
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Repository: localaiLicense: other
twil-lm3-q4
TwIL-LM3 is a 3B SmolLM3-based reasoning model specialized for formal
logic, entailment, semantic parsing, and Lean formalization. This default
entry uses the publisher's recommended Q4_K_M GGUF and supports a 65K-token
context window. A higher-quality Q8_0 build is available as a variant.
Repository: localaiLicense: webai-non-commercial-license-ver.-1.0
qwen3.6-35b-a3b-genesis-hermes-v7
Qwen3.6-35B-A3B Genesis Hermes V7 is LuffyTheFox's Apache-2.0 multimodal,
agentic derivative of HauhauCS's uncensored Qwen3.6-35B-A3B model. It
combines Genesis tensor calibration with Hermes function-calling data while
retaining the 35B mixture-of-experts architecture, roughly 3B active
parameters per token, and the native 262K-token context window.
This entry's own payload uses the model card's recommended APEX GGUF and the
shared F16 multimodal projector. Automatic variant selection may instead
choose Compact APEX, an MTP-enabled APEX build, or Q8_K_P based on serving
features and available memory. The model card recommends Jinja chat
templates and at least a 128K context for its thinking behavior.
Repository: localaiLicense: apache-2.0
qwen3.6-14b-a3b-fablevibes
Qwen3.6-14B-A3B-FableVibes is an Apache-2.0 mixture-of-experts reasoning
model distilled from Fable 5 and Claude Opus traces, with additional tool
calling and coding data. It retains Qwen 3.6 vision support while pruning
the 35B-A3B base to a 14B consumer-oriented footprint. This default entry
uses the recommended Q4_K_M GGUF quantization and its Q8_0 multimodal
projector.
Repository: localaiLicense: apache-2.0
qwen3.6-27b-fable-fusion-711-uncensored-heretic-nm-dau-neo-max-mtp
Important: This is the first fine tune to exceed 700 "arc-c" (The OpenAI, Claude and Gemini "zone of intelligence") in both 8 bit and 4 bit. This repo contains both "regular" and "MTP" Neo MAX Imatrix quants.
Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF
The strongest, smartest open source multi-stage model fine tune for consumer hardware ever and BUILT on consumer hardware via Unsloth.
The first model of this size/type to breach "700" ARC-C in both 8 bit and 4 bit; hench the "711" in the name.
This model (both 4 bit and 8 bit) exceeds the base Qwen 3.6 27B in 6 out of 7 benchmarks, and matches it on the 7th AND exceeds all 7 benchmarks for Qwen3.6-35B-A3B.
The 700 "intelligence club" is reserved for OpenAI, Claude and Gemini closed source models.
This is the one they fear.
This is a multi-stage fine tune, multi-fine tune, and multi-stage merge.
A Colab between myself (multiple fine tunes, including multi-stage), Nightmedia (merge/benching), TeichAI (Polaris Dataset), armand0e (Light fable 5 traces) and trohrbaugh (heretic'ing the model).
...
Repository: localaiLicense: apache-2.0
qwopus3.6-35b-a3b-coder-mtp
# 🌟 Qwopus3.6-35B-A3B-v1
## 💡 Base Model Overview
**Qwen3.6-35B-A3B** is an advanced hybrid sparse MoE (Mixture-of-Experts) model developed by Alibaba Cloud. It features 35B total parameters with only 3B active parameters per token, ensuring high inference efficiency. Architecturally, it combines Gated DeltaNet linear attention with standard gated attention layers, routing tokens across **256 experts**. It natively supports a massive **262k context window** and is specifically designed for high-performance agentic coding, deep reasoning, and multimodal tasks.
## 🚀 Model Refinement & Logic Tuning （Qwopus3.6-35B-A3B-v1）
🪐**Qwopus3.6-35B-A3B-v1** is a reasoning-enhanced MoE (Mixture of Experts) model fine-tuned on top of **Qwen3.6-35B-A3B**.
### 🛠 Training Strategy
The fine-tuning process for this model is structured into **three distinct stages of distributed SFT (Supervised Fine-Tuning)**, progressively scaling reasoning complexity and data diversity. This systematic approach ensures the model inherits the base MoE capabilities while sharpening its logic-handling depth.
...
Repository: localaiLicense: apache-2.0
agents-a1-4b
Agents-A1-4B is InternScience's Apache-2.0 dense 4B agentic model, based on
Qwen3.5. It is trained for long-horizon search, engineering and scientific
research, instruction following, tool use, and multimodal tasks. This entry
uses the official Q4_K_M GGUF quantization and vision projector.
Repository: localaiLicense: apache-2.0
agents-a1-4b-q8
Agents-A1-4B is InternScience's Apache-2.0 dense 4B agentic model, based on
Qwen3.5. It is trained for long-horizon search, engineering and scientific
research, instruction following, tool use, and multimodal tasks. This entry
uses the official Q8_0 GGUF quantization and vision projector.
Repository: localaiLicense: apache-2.0
qwopus3.6-27b-coder-compat-mtp
🪐 Qwopus-3.6-27B-Coder
Coder SFT Release
Agentic Coding & Tool-Use Reasoning Model Fine-Tuned on Qwopus3.6-27B-v2
🧬 Trace Inversion & Negentropy
🧠 27B Dense Model
⚡ Agentic Coding
🛠️ Tool Calling & Agent
🏆 SWE-bench Verified: 67.0% (off-thinking)
💡 What is Qwopus-3.6-27B-Coder?
🪐 Qwopus-3.6-27B-Coder is a reasoning-enhanced agentic coding model built on top of Qwopus3.6-27B-v2. It inherits the powerful reasoning foundation of the v2 base — which achieved 87.43% MMLU-Pro and 75.25% SWE-bench Verified — and further specializes it for agentic code generation, structured tool calling, debugging, and instruction-following in developer workflows. The model is designed to excel at repository-level coding tasks, multi-turn tool orchestration, and complex logical reasoning under realistic agent environments.
🧩 Agentic Coding
Optimized for repository-level coding, debugging, patch generation, and structured multi-step development workflows.
🛠️ Tool Calling
Learns from real agent trajectories with tool definitions, tool calls, and environment feedback for robust multi-turn execution.
...
Repository: localaiLicense: apache-2.0
qwen3.5-9b-hauhaucs-aggressive
Qwen3.5 9B Aggressive is HauhauCS's refusal-removed fine-tune of the
multimodal Qwen3.5 9B model. It retains the base model's reasoning, tool
use, image and video understanding, and 262K-token native context window.
This entry uses the balanced Q4_K_M GGUF quantization and includes the
matching BF16 multimodal projector. The Q8_0 variant offers higher fidelity.
Repository: localaiLicense: apache-2.0
qwen3.5-9b-hauhaucs-aggressive-q8
Qwen3.5 9B Aggressive is HauhauCS's refusal-removed fine-tune of the
multimodal Qwen3.5 9B model. It retains the base model's reasoning, tool
use, image and video understanding, and 262K-token native context window.
This entry uses the higher-fidelity Q8_0 GGUF quantization and includes the
matching BF16 multimodal projector.
Repository: localaiLicense: apache-2.0
qwythos-9b-claude-mythos-5-1m
# Qwythos-9B
**Developed by Empero**
**Qwythos-9B** is a full-parameter reasoning model built on top of a **deeply uncensored Qwen3.5-9B base** and post-trained on **over 500 million tokens** of high-quality Claude Mythos and Claude Fable traces, with chain-of-thought generated in-house by Empero AI's internal tool **rethink**.
The result is a compact, fast, **dramatically more capable** 9B reasoning model. Headline capabilities:
...
Repository: localaiLicense: apache-2.0
qwopus3.6-27b-coder-mtp-nvfp4
🪐 Qwopus-3.6-27B-Coder
Coder SFT Release
Agentic Coding & Tool-Use Reasoning Model Fine-Tuned on Qwopus3.6-27B-v2
🧬 Trace Inversion & Negentropy
🧠 27B Dense Model
⚡ Agentic Coding
🛠️ Tool Calling & Agent
🏆 SWE-bench Verified: 67.0% (off-thinking)
💡 What is Qwopus-3.6-27B-Coder?
🪐 Qwopus-3.6-27B-Coder is a reasoning-enhanced agentic coding model built on top of Qwopus3.6-27B-v2. It inherits the powerful reasoning foundation of the v2 base — which achieved 87.43% MMLU-Pro (300ex) and 75.25% SWE-bench Verified — and further specializes it for agentic code generation, structured tool calling, debugging, and instruction-following in developer workflows. The model is designed to excel at repository-level coding tasks, multi-turn tool orchestration, and complex logical reasoning under realistic agent environments.
🧩 Agentic Coding
Optimized for repository-level coding, debugging, patch generation, and structured multi-step development workflows.
🛠️ Tool Calling
Learns from real agent trajectories with tool definitions, tool calls, and environment feedback for robust multi-turn execution.
...
Repository: localai
gemma-4-31b-scotoma-2-q4
Gemma 4 31B Scotoma 2 is a multimodal Gemma 4 31B instruction-tuned model from ReadyArt. It applies a bounded refusal edit and preference training intended to reduce repetitive prose patterns while retaining the base model's text and image capabilities. This entry uses the 18.7 GB Q4_K_M GGUF and the matching Q8_0 vision projector.
License: Apache 2.0 | Base model: Google Gemma 4 31B IT
Repository: localaiLicense: apache-2.0
gemma-4-31b-scotoma-2-q8
Gemma 4 31B Scotoma 2 is a multimodal Gemma 4 31B instruction-tuned model from ReadyArt. It applies a bounded refusal edit and preference training intended to reduce repetitive prose patterns while retaining the base model's text and image capabilities. This higher-fidelity entry uses the 32.6 GB Q8_0 GGUF and the matching Q8_0 vision projector.
License: Apache 2.0 | Base model: Google Gemma 4 31B IT
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
qwopus3.5-9b-coder-mtp
# 🌟 Qwopus3.5-9B-v3.5
## 💡 Model Overview & v3.5 Design
Qwopus3.5-9B-v3.5 is a **data-scaled continuation** of the Qwopus3.5-9B-v3 model.
The training data in v3.5 is expanded to cover a broader range of domains, including mathematics, programming, puzzle-solving, multilingual dialogue, instruction-following, multi-turn interactions, and STEM-related tasks.
Qwopus3.5-9B-v3.5 is a reasoning-enhanced model based on **Qwen3.5-9B**, designed for:
- 🧩 Structured reasoning
- 🔧 Tool-augmented workflows
- 🔁 Multi-step agentic tasks
- ⚡ Token-efficient inference
Compared with Qwopus3.5-9B-v3, **3.5 version does not introduce a new architecture, RL stage, or template redesign**.
This version is trained with approximately **2× more SFT data**.
## 🎯 Motivation & Generalization Insight
The motivation behind v3.5 comes from a simple observation:
> This work is motivated by the hypothesis that scaling high-quality SFT data may further enhance the generalization ability of large language models.
In earlier Qwopus3.5 experiments, structured reasoning was observed to improve both **accuracy and efficiency**:
...
Repository: localaiLicense: apache-2.0
qwen3.6-40b-claude-4.6-opus-deckard-heretic-uncensored-thinking-neo-code-di-imatrix-max
The Qwen 3.5 version (also 40B) got 181 likes+ This version uses the new Qwen 3.6 27B arch (which exceeds even Qwen's own 398B model).
WARNING: This model has character and intelligence. It will take no prisoners. It will give no quarter. Uncensored,
Unfiltered and boldly confident. Not even remotely "SFW", if you ask it for NSFW content. And it is wickedly smart too - exceeding the base model in 6 out of 7 benchmarks.
Qwen3.6-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking
40 billion parameters (dense, not moe) expanded from 27B Qwen 3.6, then trained on Claude 4.6 Opus High Reasoning dataset via Unsloth on local hardware... but there
is much more to the story - in comes DECKARD.
96 layers, 1275 Tensors. (50% more than base model of 27B)
Features variable length reasoning ; less complex = shorter, longer for more complex.
Model performance has increased dramatically. And it has character too.
A lot of character.
No censorship, no nanny. (via Heretic)
And it is very, very smart.
...
Repository: localaiLicense: apache-2.0
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
xyz-aquila-mini
XYZ-Aquila-mini is an Apache-2.0, open-weight thinking model based on
Qwen3.6-35B-A3B. It is tuned for agentic deep search, long-horizon planning,
bilingual web research, evidence aggregation, source verification, and
recovery from failed tool interactions. This entry uses the Q4_K_M GGUF;
the Q6_K variant offers higher fidelity on hosts with more memory.
The checkpoint supports Qwen-compatible reasoning and tool-call formats.
Search, scraping, and Python execution are supplied by the agent harness,
not by the model weights themselves.
Repository: localaiLicense: apache-2.0
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