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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
minicpm5-1b-claude-opus-fable5-v2-thinking
# MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking
GGUF quantizations for local deployment: **MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-GGUF**
中文说明
**MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking** is a compact 1B **Thinking** language model built on openbmb/MiniCPM5-1B. Compared with V1, this V2 release is further fine-tuned on **Fable 5** data with a stronger focus on **tool calling / function calling**, while also improving **coding** and **instruction-following**. It keeps MiniCPM5's native Thinking chat template and XML tool-call format.
Previous version: **MiniCPM5-1B-Claude-Opus-Fable5-Thinking** (V1)
For llama.cpp / Ollama / LM Studio deployment, see the **GGUF repository**.
## Overview
## Capabilities
- **Tool calling (enhanced in V2)** — more reliable XML / function-calling style tool use on top of MiniCPM5's native format
- **Coding** — code generation, debugging, and software-engineering-style tasks
- **Instruction following** — more reliable adherence to user prompts and structured constraints
- **Thinking mode** — chain-of-thought reasoning via the MiniCPM5 chat template
- **Long context** — up to **128K tokens** (131,072 tokens per `config.json`)
...
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
minicpm5-1b-claude-opus-fable5-thinking
# MiniCPM5-1B-Claude-Opus-Fable5-Thinking
GGUF quantizations for local deployment: **MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF**
中文说明
**MiniCPM5-1B-Claude-Opus-Fable5-Thinking** is a compact 1B **Thinking** language model built on openbmb/MiniCPM5-1B. It is further fine-tuned on **Fable 5** data to improve **coding** and **instruction-following** while keeping MiniCPM5's native Thinking chat template and tool-call format.
For llama.cpp / Ollama / LM Studio deployment, see the **GGUF repository**.
## Overview
## Capabilities
- **Coding** — code generation, debugging, and software-engineering-style tasks
- **Instruction following** — more reliable adherence to user prompts and structured constraints
- **Thinking mode** — chain-of-thought reasoning via the MiniCPM5 chat template
- **Tool calling** — inherits MiniCPM5's XML tool-call format
- **Long context** — up to **128K tokens** (131,072 tokens per `config.json`)
## Quick start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking"
...
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
laguna-xs-2.1
Laguna XS 2.1 is Poolside's 33B-parameter, 3B-active Mixture-of-Experts model for agentic coding and long-horizon work on local machines. It supports tool use, interleaved reasoning, and a native 262K-token context window. This default entry uses the official 20.3 GB Q4_K_M GGUF.
License: OpenMDW 1.1.
Repository: localaiLicense: other
laguna-xs-2.1-apex-i-quality
Laguna XS 2.1 in the 21.8 GB APEX-I Quality format. This is the highest-fidelity importance-matrix APEX build for llama.cpp.
License: OpenMDW 1.1.
Repository: localaiLicense: other
laguna-xs-2.1-apex-i-balanced
Laguna XS 2.1 in the 24.3 GB APEX-I Balanced format, an importance-matrix build balancing fidelity and memory use for llama.cpp.
License: OpenMDW 1.1.
Repository: localaiLicense: other
laguna-xs-2.1-apex-i-compact
Laguna XS 2.1 in the 15.8 GB APEX-I Compact format, an importance-matrix build tuned for lower memory use in llama.cpp.
License: OpenMDW 1.1.
Repository: localaiLicense: other
laguna-xs-2.1-apex-i-mini
Laguna XS 2.1 in the 12.8 GB APEX-I Mini format, the smallest importance-matrix APEX build for llama.cpp.
License: OpenMDW 1.1.
Repository: localaiLicense: other
laguna-xs-2.1-apex-quality
Laguna XS 2.1 in the 21.8 GB APEX Quality format, the highest-fidelity non-imatrix APEX build for llama.cpp.
License: OpenMDW 1.1.
Repository: localaiLicense: other
laguna-xs-2.1-apex-balanced
Laguna XS 2.1 in the 24.3 GB APEX Balanced format, balancing fidelity and memory use for llama.cpp.
License: OpenMDW 1.1.
Repository: localaiLicense: other
laguna-xs-2.1-apex-compact
Laguna XS 2.1 in the 15.8 GB APEX Compact format, tuned for lower memory use in llama.cpp.
License: OpenMDW 1.1.
Repository: localaiLicense: other
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