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qwopus3.8-27b-flash
# 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.
...
Repository: localaiLicense: apache-2.0
supra2-100m-instruct
Supra2-100M-Instruct is a compact English chat model trained from scratch by
SupraLabs on the Qwen3 architecture. It has 100 million parameters, a
2,048-token context window, and is intended for lightweight experiments and
constrained edge deployments. This entry uses the publisher's official F16
GGUF build.
Repository: localaiLicense: apache-2.0
llm-jp-4-33b-thinking-q4
LLM-jp-4-33B-thinking is an Apache-2.0 Japanese and English reasoning
model from Japan's National Institute of Informatics. Its dense Llama
architecture has 33 billion parameters and a 65K-token context window.
The model was aligned with supervised fine-tuning and DPO for multi-turn
conversation and instruction following.
This default entry uses the 20.2 GB Q4_K_M GGUF. The official 66.4 GB
BF16 weights are available as a higher-fidelity variant.
Repository: localaiLicense: apache-2.0
dfm-mimir:vllm
DFM Mimir is an Apache-2.0, instruction-tuned HRM-Text model from Danish
Foundation Models. It has about 1 billion parameters and a 4,096-token
context window. The model focuses on Danish and English chat, reasoning,
mathematics, and code generation, and uses only permissible post-training
data. This entry serves the official BF16 safetensors checkpoint with vLLM.
Repository: localaiLicense: apache-2.0
hy-mt2-1.8b-q4
Hy-MT2-1.8B is Tencent's compact multilingual translation model. It
follows translation instructions across 33 languages and supports tasks
such as terminology control, style transfer, and structure-preserving
translation.
This default entry uses the 1.1 GB Q4_K_M GGUF. A higher-quality Q8_0
model is available as a variant.
Repository: localaiLicense: apache-2.0
ling-3.0-flash-iq1
Ling-3.0-flash is InclusionAI's MIT-licensed hybrid reasoning MoE model
with 124B total parameters and 5.5B active parameters per token. It targets
coding, deep research, instruction following, and agentic workflows with a
native 256K-token context window.
This default entry uses the 36.5 GB AD-IQ1_M GGUF. A higher-quality
44.7 GB AD-IQ2_XS model is available as a variant.
Repository: localaiLicense: mit
qwen3.8-9b-q4
Qwen3.8-9B is Empero AI's full-parameter distillation of Qwen3.8 2.4T
A95B into the dense Qwen3.5-9B architecture. It targets reasoning,
mathematics, coding, instruction following, and tool use, and supports a
native 262K-token context window. This default entry uses Q4_K_M weights;
a higher-quality Q8_0 build is available as a variant.
Repository: localaiLicense: apache-2.0
qwen3.8-4b-q4
Qwen3.8-4B is Empero AI's full-parameter distillation of Qwen3.8 2.4T
A95B into the Qwen3.5-4B architecture. It targets mathematics, reasoning,
instruction following, and tool use with a native 262K-token context
window. This default entry uses Q4_K_M weights; a higher-quality Q8_0
build is available as a variant.
Repository: localaiLicense: apache-2.0
qwen3.8-2b-q4
Qwen3.8-2B is Empero AI's smallest Qwen3.8 reasoning distillation. It uses
the Qwen3.5-2B architecture and targets mathematics, instruction
following, tool use, and edge deployment with a native 262K-token context
window. This default entry uses Q4_K_M weights; a higher-quality Q8_0
build is available as a variant.
Repository: localaiLicense: apache-2.0
homura-30b-q4
Homura 30B is an English, agent-focused fine-tune of Muse Glimmer 30B.
It targets autonomous tool use and direct instruction following. This
entry uses the publisher's 16.9 GB Q4_K_M GGUF and supports a 131K-token
context window.
Repository: localaiLicense: apache-2.0
qwen3.8-2b-distill-q4
Qwen3.8 2B Distill is an Apache-2.0, text-only Qwen3.5 2B fine-tune
distilled from Qwen3.8 2.4T A95B reasoning traces. It targets compact
reasoning, coding, instruction following, and function calling with a 262K
native context window. This entry uses the balanced Q4_K_M GGUF
quantization; the Q8_0 variant offers higher fidelity.
Repository: localaiLicense: apache-2.0
qwen3.8-2b-distill-q8
Qwen3.8 2B Distill in the higher-fidelity Q8_0 GGUF format. This text-only
Qwen3.5 2B fine-tune targets reasoning, coding, instruction following, and
function calling with a 262K native context window.
Repository: localaiLicense: apache-2.0
qwen3.8-4b-distill-q4
Qwen3.8 4B Distill is an Apache-2.0, text-only Qwen3.5 4B fine-tune
distilled from Qwen3.8 2.4T A95B reasoning traces. It targets reasoning,
coding, instruction following, and function calling with a 262K native
context window. This entry uses the balanced Q4_K_M GGUF quantization; the
Q8_0 variant offers higher fidelity.
Repository: localaiLicense: apache-2.0
qwen3.8-4b-distill-q8
Qwen3.8 4B Distill in the higher-fidelity Q8_0 GGUF format. This text-only
Qwen3.5 4B fine-tune targets reasoning, coding, instruction following, and
function calling with a 262K native context window.
Repository: localaiLicense: apache-2.0
qwen3.8-9b-distill-q4
Qwen3.8 9B Distill is an Apache-2.0, text-only Qwen3.5 9B fine-tune
distilled from Qwen3.8 2.4T A95B reasoning traces. It targets mathematics,
coding, instruction following, and function calling with a 262K native
context window. This entry uses the balanced Q4_K_M GGUF quantization; the
Q8_0 variant offers higher fidelity.
Repository: localaiLicense: apache-2.0
qwen3.8-9b-distill-q8
Qwen3.8 9B Distill in the higher-fidelity Q8_0 GGUF format. This text-only
Qwen3.5 9B fine-tune targets reasoning, coding, instruction following, and
function calling with a 262K native context window.
Repository: localaiLicense: apache-2.0
instella-moe-16b-a3b-think
AMD Instella-MoE-16B-A3B-Think is a reasoning and instruction-following
mixture-of-experts model with 16 billion total parameters and 3 billion
active parameters. It supports long-form reasoning, chat, coding, and tool
use. This entry uses the Q4_K_M GGUF quantization.
Repository: localaiLicense: other
instella-moe-16b-a3b-think-q8
AMD Instella-MoE-16B-A3B-Think is a reasoning and instruction-following
mixture-of-experts model with 16 billion total parameters and 3 billion
active parameters. It supports long-form reasoning, chat, coding, and tool
use. This entry uses the near-lossless Q8_0 GGUF quantization.
Repository: localaiLicense: other
mellum2-12b-a2.5b-instruct
Mellum2-12B-A2.5B-Instruct is an Apache-2.0 mixture-of-experts model from
JetBrains with 12 billion total parameters, 2.5 billion activated per token,
and a 131,072-token context window.
This entry uses the Q4_K_M GGUF quantization.
Repository: localaiLicense: apache-2.0
mellum2-12b-a2.5b-instruct-q8
Mellum2-12B-A2.5B-Instruct is an Apache-2.0 mixture-of-experts model from
JetBrains with 12 billion total parameters, 2.5 billion activated per token,
and a 131,072-token context window.
This entry uses the higher-quality Q8_0 GGUF quantization.
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
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