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huihui-qwen3.8-27b-abliterated
# Qwen3.8-27B
> [!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, TokenSpeed, etc.
> [!Tip]
> For users seeking managed, scalable inference without infrastructure maintenance, the official Qwen API service is provided by Qwen Cloud.
> In particular, **Qwen3.8-27B** will be available as a hosted version with more production features, e.g., 1M context length by default, official built-in tools. For more information, please refer to the Qwen3.8-27B Overview. The service is coming soon. Stay tuned for updates.
Following the widespread community adoption of the Qwen3.5 and Qwen3.6 series, we are pleased to introduce Qwen3.8, the most capable generation in the Qwen open-model family to date.
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Repository: localaiLicense: apache-2.0
qwen3.8-27b-q4-mtp
Qwen3.8-27B with the official Q4_K_M model and Q4_0 MTP draft model.
MTP speculative decoding can increase generation speed by proposing
multiple tokens for the target model to verify.
Repository: localaiLicense: apache-2.0
qwen3.8-27b-ridge
Qwen3.8-27B Ridge is a 3.69-bit mixed quantization that keeps the
Gated-DeltaNet state path at Q8_0 and preserves the embedded MTP head.
It reduces the model weights to 12.59 GB while retaining multimodal,
reasoning, coding, tool-use, and long-context capabilities.
Repository: localaiLicense: apache-2.0
qwen3.5-9b-defiant-fable-mtp
Qwen3.5 9B Defiant Fable is an Apache-2.0 multimodal fine-tune for
reasoning, coding, creative writing, and roleplay. It retains the 256K
context window and vision support of Qwen3.5 while reducing refusals.
This default entry uses the NEO-imatrix Q4_K_M build with multi-token
prediction enabled for faster generation.
Repository: localaiLicense: apache-2.0
grug-27b-mtp
Grug 27B MTP is the Q4_K_M GGUF build with multi-token prediction enabled
for speculative decoding, plus the shared vision projector.
Repository: localaiLicense: apache-2.0
qwen3.6-35b-a3b-genesis-hermes-v7-mtp-apex
Qwen3.6-35B-A3B Genesis Hermes V7 in the full APEX GGUF format with native
multi-token prediction enabled for speculative decoding, plus the shared
F16 multimodal projector.
Repository: localaiLicense: apache-2.0
qwen3.6-35b-a3b-genesis-hermes-v7-mtp-apex-compact
Qwen3.6-35B-A3B Genesis Hermes V7 in the smaller APEX Compact GGUF format
with native multi-token prediction enabled for speculative decoding, plus
the shared F16 multimodal projector.
Repository: localaiLicense: apache-2.0
qwythos-27b-v1-mtp
Qwythos-27B-v1 MTP is the Q4_K_M build with its native multi-token
prediction head enabled for faster speculative decoding. It also includes
the shared vision projector and supports tool use and long-context
reasoning.
Repository: localaiLicense: apache-2.0
qwythos-9b-v2
Empero AI
# Qwythos-9B-v2 β the new and improved Qwythos
The next iteration of Qwythos: **all the reasoning of Qwythos-9B, with the looping behavior fixed.** v2 keeps the deep chain-of-thought, the uncensored research posture, and the 1M-token context of its predecessor, and cleans up the rough edges that showed up in real use.
- π **Looping behavior eliminated** β repetition/degeneration under greedy or low-temperature decoding dropped from **6.7% β 0%**. You can serve it *without* leaning on `repetition_penalty` as a band-aid.
- π§ **Reasoning fully preserved** β MMLU, GSM8K, GPQA, ARC and HumanEval are all held at (or above) the v1 level. This is a *hygiene* upgrade, not a capability regression.
- 𧩠**MTP head restored** β the native multi-token-prediction module (dropped in the previous export) is back, so config and weights agree and speculative-decoding setups work.
- πͺͺ **Cleaner identity** β the model no longer prefaces unrelated answers with its identity; it introduces itself only when you actually ask.
- π **Still intentionally uncensored** for research, cybersecurity, red-teaming, biology, chemistry, pharmacology and clinical work.
- π **St
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Repository: localaiLicense: apache-2.0
tess-4-27b-mtp
Tess-4-27B with its Q4_K_M multi-token prediction draft enabled for
speculative decoding. The main model verifies every proposed token, and
the entry also includes the shared F16 vision 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).
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Repository: localaiLicense: apache-2.0
hy3
δΈζΒ ο½Β English
[](#license)
Β Β
[](https://huggingface.co/tencent/Hy3)
Β Β
[](https://modelscope.cn/models/Tencent-Hunyuan/Hy3)
Β Β
[](https://cnb.cool/ai-models/tencent/Hy3)
Β Β
[](https://ai.gitcode.com/tencent_hunyuan/Hy3)
π₯οΈΒ Official WebsiteΒ Β |Β Β
π¬Β GitHub
## Table of Contents
- Model Introduction
- Stronger Agent Capabilities
- More Reliable Product Experiences
- Benchmark Appendix
- News
- Model Links
- Quickstart
- Deployment
- vLLM
- SGLang
- Finetuning
- RL Post-training
- Quantization
- License
- Contact Us
## Model Introduction
**Hy3** is a 295B-parameter Mixture-of-Experts (MoE) model with 21B active parameters and 3.8B MTP layer parameters, developed by the Tencent Hy Team. Following the Hy3 Preview launch in late April, we gathered feedback from 50+ products and scaled up post-training with higher quality data. Today, we introduce Hy3, which outperforms similar-size models and rivals flagship open-source models with 2-5x parameters. It also shows significant gains in utility across various products and productivity tasks.
## Stronger Agent Capabilities
...
Repository: localaiLicense: apache-2.0
qwopus3.6-35b-a3b-coder-mtp
# π Qwopus3.6-35B-A3B-v1
## π‘ 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.
## π Model Refinement & Logic Tuning οΌQwopus3.6-35B-A3B-v1οΌ
πͺ**Qwopus3.6-35B-A3B-v1** is a reasoning-enhanced MoE (Mixture of Experts) model fine-tuned on top of **Qwen3.6-35B-A3B**.
### π 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.
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Repository: localaiLicense: apache-2.0
ornith-1.0-9b-mtp
[](https://deep-reinforce.com/ornith.html)
# Ornith-1.0-9B
Aloha! πΊ Today, we are releasing Ornith-1.0, a self-improving family of open-source models for agentic coding.
Highlights:
- **State-of-the-Art Coding Agents**: Available in 9B-Dense, 31B-Dense, 35B-MoE, and 397B-MoE (post-trained on top of Gemma 4 and Qwen 3.5), achieving state-of-the-art performance among open-source models of comparable size on coding benchmarks such as Terminal-Bench 2.1, SWE-Bench, NL2Repo and OpenClaw.
- **Self-Improving Training Framework**: Β Ornith-1.0 employs RL to learn to generate not only solution rollouts, but also the scallfold that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model discovers better search trajectories and generates higher-quality solutions.
- **Licence**: MIT licensed, globally accessible, and free from regional limitations.
## Ornith 1.0 9B
This model card documents **Ornith-1.0-9B**, the most lightweight member of the Ornith family, designed for efficient single-GPU deployment.
### Benchmarks
Ornith-1.0-9B
Qwen3.5-9B
Qwen3.5-35B
Gemma4-12B
Gemma4-31B
Agentic Coding
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Repository: localaiLicense: mit
gemmable-4-12b-mtp
## Gemmable 4 12B
Gemmable 4 12B is a GGUF export of Gemma 4 12B fine-tuned on Fable-5 style
reasoning and assistant traces.
## Highlights
- Base model: `google/gemma-4-12B`
- Format: GGUF
- Training style: Fable-5 style reasoning and assistant traces
- Distribution: fp16 GGUF plus matching assistant GGUFs for each quant
- Intended use: local inference, coding, reasoning, and assistant workflows
## How to use
### llama.cpp
Standard load:
```bash
llama-server -m "gemmable-4-12b-fp16.gguf"
```
Speculative / draft-MTP load:
```bash
llama-server -m "gemmable-4-12b-Q4_K_M.gguf" \
--spec-draft-model "gemmable-4-12b-Q4_K_M-mtp.gguf" \
--spec-type draft-mtp \
--spec-draft-n-max 4
```
Use the matching fp16 or quantized main file with its `-mtp` companion.
### LM Studio
1. Search this repo, download target + mtp file.
2. Load target.
3. Load settings β Speculative Decoding β select mtp file file.
(Requires a llama.cpp runtime with Gemma 4 MTP support from ggml-org/llama.cpp#23398. LocalAI's pinned llama.cpp backend already carries it, so this entry runs draft-mtp out of the box.)
## GGUF / local inference notes
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Repository: localai
bigbang-v1-q4-k-m
BigBang-v1 is an Apache-2.0 multimodal reasoning model fine-tuned from
Qwen3.6-35B-A3B for scientific research, coding, long-horizon search, and
tool use. It supports image input, a 262K native context window, and
built-in multi-token prediction. This entry uses the recommended Q4_K_M
GGUF quantization and the F16 vision projector.
Repository: localaiLicense: apache-2.0
bigbang-v1-q6-k
BigBang-v1 is an Apache-2.0 multimodal reasoning model fine-tuned from
Qwen3.6-35B-A3B for scientific research, coding, long-horizon search, and
tool use. It supports image input, a 262K native context window, and
built-in multi-token prediction. This entry uses the higher-quality Q6_K
GGUF quantization and the F16 vision projector.
Repository: localaiLicense: apache-2.0
qwopus3.6-27b-coder-compat-mtp
πͺ Qwopus-3.6-27B-Coder
Coder SFT Release
Agentic Coding & Tool-Use Reasoning Model Fine-Tuned on Qwopus3.6-27B-v2
𧬠Trace Inversion & Negentropy
π§ 27B Dense Model
β‘ Agentic Coding
π οΈ Tool Calling & Agent
π SWE-bench Verified: 67.0% (off-thinking)
π‘ What is Qwopus-3.6-27B-Coder?
πͺ 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 β which achieved 87.43% MMLU-Pro and 75.25% SWE-bench Verified β 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.
𧩠Agentic Coding
Optimized for repository-level coding, debugging, patch generation, and structured multi-step development workflows.
π οΈ Tool Calling
Learns from real agent trajectories with tool definitions, tool calls, and environment feedback for robust multi-turn execution.
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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:
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Repository: localaiLicense: apache-2.0
glm-5.2
# GLM-5.2
π Join our WeChat or Discord community.
π Check out the GLM-5.2 blog and GLM-5 Technical report.
π Use GLM-5.2 API services on Z.ai API Platform.
π Try GLM-5.2 here.
[Paper]
[GitHub]
## Introduction
We're introducing GLM-5.2, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on a **solid 1M-token context**. GLM-5.2's new capabilities include:
- **Solid 1M Context:** A solid 1M-token context that stably sustains long-horizon work
- **Advanced Coding with Flexible Effort**: Stronger coding capabilities with multiple thinking effort levels to balance performance and latency
- **Improved Architecture**: We propose IndexShare, which reuses the same indexer across every four sparse attention layers, reducing per-token FLOPs by 2.9Γ at a 1M context length. We also improve GLM-5.2βs MTP layer for speculative decoding, increasing the acceptance length by up to 20%
- **Pure Open**: An MIT open-source license β no regional limits, technical access without borders
## Benchmark
## Serve GLM-5.2 Locally
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Repository: localaiLicense: mit
qwen3.6-35b-a3b-nvfp4-mtp
# Qwen3.6-35B-A3B
[](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 Qwen3.6-35B-A3B.
## Model Overview
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Repository: localai
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