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kat-coder-v2.5-dev
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-q8
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry uses the higher-quality Q8_0 GGUF quantization.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-apex-i-quality
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-apex-i-balanced
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-apex-i-compact
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-apex-i-mini
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-apex-quality
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-apex-balanced
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
kat-coder-v2.5-dev-apex-compact
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot,
post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with
3 billion activated per token, a 262K-token context window, and text-only
weights tuned for repository-level coding and tool use.
This entry offers standard Q4_K_M and Q8_0 GGUF quantizations alongside
APEX mixed-precision variants with quality, balanced, compact, and mini
profiles. The APEX I-profiles use importance-matrix calibration.
Repository: localaiLicense: apache-2.0
inkling
# Inkling
BF16 |
NVFP4 |
Playground |
Tinker Cookbook |
Acceptable Use
## 1. General Information
Inkling is a general-purpose multimodal model that accepts text, image and audio inputs and generates text outputs. It is intended for use in English and other languages, and across multiple coding languages. The model is designed to be used by developers building AI-powered applications, including agentic and tool-use systems, coding assistants, chatbots, and retrieval-augmented generation systems, and is suitable for general-purpose conversational use, instruction-following, and other natural language and multimodal tasks. It is released with open weights to support research, fine-tuning and integration into third-party products by downstream developers.
**Languages:** English, with general multilingual capabilities across other languages.
## 2. Getting Started
Try Inkling on the Tinker Playground or access via API using the Tinker Cookbook.
Inkling supports local deployment using the following open-source libraries:
* SGLang (recipe, PR)
* vLLM (recipe, PR)
* TokenSpeed (recipe, PR)
* Unsloth (recipe, PR)
* Huggingface (recipe, PR)
...
Repository: localaiLicense: apache-2.0
hy3
中文 ｜ English
[](#license)
[](https://huggingface.co/tencent/Hy3)
[](https://modelscope.cn/models/Tencent-Hunyuan/Hy3)
[](https://cnb.cool/ai-models/tencent/Hy3)
[](https://ai.gitcode.com/tencent_hunyuan/Hy3)
🖥️ Official Website |
💬 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
# 🌟 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
lfm2.5-1.2b-instruct
Try LFM • Docs • LEAP • Discord
# LFM2.5-1.2B-Instruct
LFM2.5 is a new family of hybrid models designed for **on-device deployment**. It builds on the LFM2 architecture with extended pre-training and reinforcement learning.
- **Best-in-class performance**: A 1.2B model rivaling much larger models, bringing high-quality AI to your pocket.
- **Fast edge inference**: 239 tok/s decode on AMD CPU, 82 tok/s on mobile NPU. Runs under 1GB of memory with day-one support for llama.cpp, MLX, and vLLM.
- **Scaled training**: Extended pre-training from 10T to 28T tokens and large-scale multi-stage reinforcement learning.
Find more information about LFM2.5 in our blog post.
## 🗒️ Model Details
LFM2.5-1.2B-Instruct is a general-purpose text-only model with the following features:
...
Repository: localaiLicense: other
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
qwen3.6-35b-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
...
Repository: localai
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
qwen3.6-27b-nvfp4-mtp
# 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: localai
gemma-4-12b-agentic-fable5-composer2.5-v2-3.5x-tau2
Hugging Face |
GitHub |
Launch Blog |
Documentation
License: Apache 2.0 | Authors: Google DeepMind
> [!Note]
> This model card is for the Gemma 4 12B Unified model, which is part of the Gemma 4 family of open models. Built with the same multimodal functionality as Gemma 4 E2B and E4B (text, audio, image, and video inputs), it brings native audio and vision understanding directly to local environments without the need for separate encoders. This unified approach to multimodality makes the model encoder-free, offering a deployment size that is perfect for consumer devices and streamlined local execution.
Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages.
...
Repository: localaiLicense: apache-2.0
qwen3.6-27b-mtp-pi-tune
# 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
gemma-4-12b-coder-fable5-composer2.5-v1
Hugging Face |
GitHub |
Launch Blog |
Documentation
License: Apache 2.0 | Authors: Google DeepMind
> [!Note]
> This model card is for the Gemma 4 12B Unified model, which is part of the Gemma 4 family of open models. Built with the same multimodal functionality as Gemma 4 E2B and E4B (text, audio, image, and video inputs), it brings native audio and vision understanding directly to local environments without the need for separate encoders. This unified approach to multimodality makes the model encoder-free, offering a deployment size that is perfect for consumer devices and streamlined local execution.
Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages.
...
Repository: localaiLicense: gemma
gemma-4-e2b-it-qat-q4_0
Gemma 4 E2B is a multimodal (text + image) instruction-tuned model from Google DeepMind, optimized with Quantization-Aware Training (QAT) to preserve bfloat16-level quality at a fraction of the memory. E2B is a MatFormer "effective 2B" elastic variant: it carries a larger backbone but runs at an effective 2B-parameter footprint, making it well suited to lightweight and on-device deployments. This is the official Google Q4_0 GGUF, shipped with its multimodal projector.
License: Apache 2.0 | Authors: Google DeepMind
Repository: localaiLicense: apache-2.0
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