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qwopus3.6-35b-a3b-v1
# 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
...
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
qwen3.5-9b-deepseek-v4-flash
# Qwen3.5-9B
[](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.
Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency.
## Qwen3.5 Highlights
Qwen3.5 features the following enhancement:
- **Unified Vision-Language Foundation**: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.
- **Efficient Hybrid Architecture**: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.
...
Repository: localaiLicense: apache-2.0
nemotron-3-nano-omni-30b-a3b-reasoning-apex
# Model Overview
### Description:
NVIDIA Nemotron 3 Nano Omni is a multimodal large language model that unifies video, audio, image, and text understanding to support enterprise-grade Q&A, summarization, transcription, and document intelligence workflows. It extends the Nemotron Nano family with integrated video+speech comprehension, Graphical User Interface (GUI), Optical Character Recognition (OCR), and speech transcription capabilities, enabling end-to-end processing of rich enterprise content such as meeting recordings, M&E assets, training videos, and complex business documents. NVIDIA Nemotron 3 Nano Omni was developed by NVIDIA as part of the Nemotron model family.
This model is available for commercial use.
This model was improved using Qwen3-VL-30B-A3B-Instruct, Qwen3.5-122B-A10B, Qwen3.5-397B-A17B, Qwen2.5-VL-72B-Instruct, and gpt-oss-120b. For more information, please see the Training Dataset section below.
### License/Terms of Use
Governing Terms: Use of this model is governed by the NVIDIA Open Model Agreement
### Deployment Geography:
Global
...
Repository: localaiLicense: other
kimi-k2.6
🤗 huggingchat
|
📰 Tech Blog
## 1. Model Introduction
Kimi K2.6 is an open-source, native multimodal agentic model that advances practical capabilities in long-horizon coding, coding-driven design, proactive autonomous execution, and swarm-based task orchestration.
### Key Features
- **Long-Horizon Coding**: K2.6 achieves significant improvements on complex, end-to-end coding tasks, generalizing robustly across programming languages (Rust, Go, Python) and domains spanning front-end, DevOps, and performance optimization.
- **Coding-Driven Design**: K2.6 is capable of transforming simple prompts and visual inputs into production-ready interfaces and lightweight full-stack workflows, generating structured layouts, interactive elements, and rich animations with deliberate aesthetic precision.
- **Elevated Agent Swarm**: Scaling horizontally to 300 sub-agents executing 4,000 coordinated steps, K2.6 can dynamically decompose tasks into parallel, domain-specialized subtasks, delivering end-to-end outputs from documents to websites to spreadsheets in a single autonomous run.
- **Proactive & Open Orchestration**: For autonomous tasks, K2.6 demonstra
...
Repository: localaiLicense: modified-mit
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
qwopus-glm-18b-merged
# 🪐 Qwen3.5-9B-GLM5.1-Distill-v1
## 📌 Model Overview
**Model Name:** `Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1`
**Base Model:** Qwen3.5-9B
**Training Type:** Supervised Fine-Tuning (SFT, Distillation)
**Parameter Scale:** 9B
**Training Framework:** Unsloth
This model is a distilled variant of **Qwen3.5-9B**, trained on high-quality reasoning data derived from **GLM-5.1**.
The primary goals are to:
- Improve **structured reasoning ability**
- Enhance **instruction-following consistency**
- Activate **latent knowledge via better reasoning structure**
## 📊 Training Data
### Main Dataset
- `Jackrong/GLM-5.1-Reasoning-1M-Cleaned`
- Cleaned from the original `Kassadin88/GLM-5.1-1000000x` dataset.
- Generated from a **GLM-5.1 teacher model**
- Approximately **700x** the scale of `Qwen3.5-reasoning-700x`
- Training used a **filtered subset**, not the full source dataset.
### Auxiliary Dataset
- `Jackrong/Qwen3.5-reasoning-700x`
...
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
qwen3.6-35b-a3b-claude-4.6-opus-reasoning-distilled
# 🔥 Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled
A reasoning SFT fine-tune of `Qwen/Qwen3.6-35B-A3B` on chain-of-thought (CoT) distillation mostly sourced from Claude Opus 4.6. The goal is to preserve Qwen3.6's strong agentic coding and reasoning base while nudging the model toward structured Claude Opus-style reasoning traces and more stable long-form problem solving.
The training path is text-only. The Qwen3.6 base architecture includes a vision encoder, but this fine-tuning run did not train on image or video examples.
- **Developed by:** @hesamation
- **Base model:** `Qwen/Qwen3.6-35B-A3B`
- **License:** apache-2.0
This fine-tuning run is inspired by Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled, including the notebook/training workflow style and Claude Opus reasoning-distillation direction.
[](https://x.com/Hesamation) [](https://discord.gg/vtJykN3t)
## Benchmark Results
The MMLU-Pro pass used 70 total questions per model: `--limit 5` across 14 MMLU-Pro subjects. Treat this as a smoke/comparative check, not a release-quality full benchmark.
...
Repository: localaiLicense: apache-2.0
qwen3.5-9b-glm5.1-distill-v1
# 🪐 Qwen3.5-9B-GLM5.1-Distill-v1
## 📌 Model Overview
**Model Name:** `Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1`
**Base Model:** Qwen3.5-9B
**Training Type:** Supervised Fine-Tuning (SFT, Distillation)
**Parameter Scale:** 9B
**Training Framework:** Unsloth
This model is a distilled variant of **Qwen3.5-9B**, trained on high-quality reasoning data derived from **GLM-5.1**.
The primary goals are to:
- Improve **structured reasoning ability**
- Enhance **instruction-following consistency**
- Activate **latent knowledge via better reasoning structure**
## 📊 Training Data
### Main Dataset
- `Jackrong/GLM-5.1-Reasoning-1M-Cleaned`
- Cleaned from the original `Kassadin88/GLM-5.1-1000000x` dataset.
- Generated from a **GLM-5.1 teacher model**
- Approximately **700x** the scale of `Qwen3.5-reasoning-700x`
- Training used a **filtered subset**, not the full source dataset.
### Auxiliary Dataset
- `Jackrong/Qwen3.5-reasoning-700x`
...
Repository: localaiLicense: apache-2.0
qwopus-glm-18b-merged
# 🪐 Qwen3.5-9B-GLM5.1-Distill-v1
## 📌 Model Overview
**Model Name:** `Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1`
**Base Model:** Qwen3.5-9B
**Training Type:** Supervised Fine-Tuning (SFT, Distillation)
**Parameter Scale:** 9B
**Training Framework:** Unsloth
This model is a distilled variant of **Qwen3.5-9B**, trained on high-quality reasoning data derived from **GLM-5.1**.
The primary goals are to:
- Improve **structured reasoning ability**
- Enhance **instruction-following consistency**
- Activate **latent knowledge via better reasoning structure**
## 📊 Training Data
### Main Dataset
- `Jackrong/GLM-5.1-Reasoning-1M-Cleaned`
- Cleaned from the original `Kassadin88/GLM-5.1-1000000x` dataset.
- Generated from a **GLM-5.1 teacher model**
- Approximately **700x** the scale of `Qwen3.5-reasoning-700x`
- Training used a **filtered subset**, not the full source dataset.
### Auxiliary Dataset
- `Jackrong/Qwen3.5-reasoning-700x`
...
Repository: localaiLicense: apache-2.0
qwen3.6-35b-a3b-apex
# 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
...
Repository: localaiLicense: apache-2.0
qwen3.6-35b-a3b
# 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
...
Repository: localaiLicense: apache-2.0
gemma-4-e2b-it
Google Gemma 4 E2B-IT is a lightweight open-source multimodal model with 5B total parameters and 2B effective parameters using selective parameter activation. It handles text and image input, generating text output, with a 256K context window and support for 140+ languages. Optimized for efficient execution on low-resource devices including mobile and laptops.
Repository: localaiLicense: apache-2.0
qwen_qwen3.5-2b
Qwen3.5-2B is a highly efficient, instruction-tuned multilingual language model available in various quantized GGUF formats. Optimized for llama-cpp inference, it supports chat and completion tasks with strong performance on low-RAM hardware. The model is available in multiple quantization levels ranging from Q8_0 to IQ2_M to balance quality and resource usage.
Repository: localaiLicense: apache-2.0
voxtral-mini-4b-realtime
Voxtral Mini 4B Realtime is a speech-to-text model from Mistral AI. It is a 4B parameter model optimized for fast, accurate audio transcription with low latency, making it ideal for real-time applications. The model uses the Voxtral architecture for efficient audio processing.
Repository: localaiLicense: apache-2.0
huihui-glm-4.7-flash-abliterated-i1
The model is a quantized version of **huihui-ai/Huihui-GLM-4.7-Flash-abliterated**, optimized for efficiency and deployment. It uses GGUF files with various quantization levels (e.g., IQ1_M, IQ2_XXS, Q4_K_M) and is designed for tasks requiring low-resource deployment. Key features include:
- **Base Model**: Huihui-GLM-4.7-Flash-abliterated (unmodified, original model).
- **Quantization**: Supports IQ1_M to Q4_K_M, balancing accuracy and efficiency.
- **Use Cases**: Suitable for applications needing lightweight inference, such as edge devices or resource-constrained environments.
- **Downloads**: Available in GGUF format with varying quality and size (e.g., 0.2GB to 18.2GB).
- **Tags**: Abliterated, uncensored, and optimized for specific tasks.
This model is a modified version of the original GLM-4.7, tailored for deployment with quantized weights.
Repository: localaiLicense: mit
qwen3-vl-reranker-8b
**Model Name:** Qwen3-VL-Reranker-8B
**Base Model:** Qwen/Qwen3-VL-Reranker-8B
**Description:**
A high-performance multimodal reranking model for state-of-the-art cross-modal search. It supports 30+ languages and handles text, images, screenshots, videos, and mixed modalities. With 8B parameters and a 32K context length, it refines retrieval results by combining embedding vectors with precise relevance scores. Optimized for efficiency, it supports quantized versions (e.g., Q8_0, Q4_K_M) and is ideal for applications requiring accurate multimodal content matching.
**Key Features:**
- **Multimodal**: Text, images, videos, and mixed content.
- **Language Support**: 30+ languages.
- **Quantization**: Available in Q8_0 (best quality), Q4_K_M (fast, recommended), and lower-precision options.
- **Performance**: Outperforms base models in retrieval tasks (e.g., JinaVDR, ViDoRe v3).
- **Use Case**: Enhances search pipelines by refining embeddings with precise relevance scores.
**Downloads:**
- [GGUF Files](https://huggingface.co/mradermacher/Qwen3-VL-Reranker-8B-GGUF) (e.g., `Qwen3-VL-Reranker-8B.Q8_0.gguf`).
**Usage:**
- Requires `transformers`, `qwen-vl-utils`, and `torch`.
- Example: `from scripts.qwen3_vl_reranker import Qwen3VLReranker; model = Qwen3VLReranker(...)`
**Citation:**
@article{qwen3vlembedding, ...}
This description emphasizes its capabilities, efficiency, and versatility for multimodal search tasks.
Repository: localaiLicense: apache-2.0
qwen3-vl-reranker-2b-i1
**Model Name:** Qwen3-VL-Reranker-2B-i1
**Base Model:** Qwen/Qwen3-VL-Reranker-2B
**Description:**
A high-performance multimodal reranking model for state-of-the-art cross-modal search. It supports 30+ languages and handles text, images, screenshots, videos, and mixed modalities. With 8B parameters and a 32K context length, it refines retrieval results by combining embedding vectors with precise relevance scores. Optimized for efficiency, it supports quantized versions (e.g., Q8_0, Q4_K_M) and is ideal for applications requiring accurate multimodal content matching.
**Key Features:**
- **Multimodal**: Text, images, videos, and mixed content.
- **Language Support**: 30+ languages.
- **Quantization**: Available in Q8_0 (best quality), Q4_K_M (fast, recommended), and lower-precision options.
- **Performance**: Outperforms base models in retrieval tasks (e.g., JinaVDR, ViDoRe v3).
- **Use Case**: Enhances search pipelines by refining embeddings with precise relevance scores.
**Downloads:**
- [GGUF Files](https://huggingface.co/mradermacher/Qwen3-VL-Reranker-2B-i1-GGUF) (e.g., `Qwen3-VL-Reranker-2B.i1-Q4_K_M.gguf`).
**Usage:**
- Requires `transformers`, `qwen-vl-utils`, and `torch`.
- Example: `from scripts.qwen3_vl_reranker import Qwen3VLReranker; model = Qwen3VLReranker(...)`
**Citation:**
@article{qwen3vlembedding, ...}
This description emphasizes its capabilities, efficiency, and versatility for multimodal search tasks.
Repository: localaiLicense: apache-2.0
iquest-coder-v1-40b-instruct-i1
The **IQuest-Coder-V1-40B-Instruct-i1-GGUF** is a quantized version of the original **IQuestLab/IQuest-Coder-V1-40B-Instruct** model, designed for efficient deployment. It is an **instruction-following large language model** with 40 billion parameters, optimized for tasks like code generation and reasoning.
**Key Features:**
- **Size:** 40B parameters (quantized for efficiency).
- **Purpose:** Instruction-based coding and reasoning.
- **Format:** GGUF (supports multi-part files).
- **Quantization:** Uses advanced techniques (e.g., IQ3_M, Q4_K_M) for balance between performance and quality.
**Available Quantizations:**
- Optimized for speed and size: **i1-Q4_K_M** (recommended).
- Lower-quality options for trade-off between size/quality.
**Note:** This is a **quantized version** of the original model, but the base model (IQuestLab/IQuest-Coder-V1-40B-Instruct) is the official source. For full functionality, use the unquantized version or verify compatibility with your deployment tools.
Repository: localaiLicense: iquestcoder
onerec-8b
The model `mradermacher/OneRec-8B-GGUF` is a quantized version of the base model `OpenOneRec/OneRec-8B`, a large language model designed for tasks like recommendations or content generation. It is optimized for efficiency with various quantization schemes (e.g., Q2_K, Q4_K, Q8_0) and available in multiple sizes (3.5–9.0 GB). The model uses the GGUF format and is licensed under Apache-2.0. Key features include:
- **Base Model**: `OpenOneRec/OneRec-8B` (a pre-trained language model for recommendations).
- **Quantization**: Supports multiple quantized variants (Q2_K, Q3_K, Q4_K, etc.), with the best quality for `Q4_K_S` and `Q8_0`.
- **Sizes**: Available in sizes ranging from 3.5 GB (Q2_K) to 9.0 GB (Q8_0), with faster speeds for lower-bit quantized versions.
- **Usage**: Compatible with GGUF files, suitable for deployment in applications requiring efficient model inference.
- **Licence**: Apache-2.0, available at [https://huggingface.co/OpenOneRec/OneRec-8B/blob/main/LICENSE](https://huggingface.co/OpenOneRec/OneRec-8B/blob/main/LICENSE).
For detailed specifications, refer to the [model page](https://hf.tst.eu/model#OneRec-8B-GGUF).
Repository: localaiLicense: apache-2.0
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