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spark-x2.5-4b
# Spark-X2.5
[](https://join.slack.com/t/tokenspark/shared_invite/zt-432qf8l2f-5~dLyXv8uETr0P0UuC07nw)
[](https://discord.gg/kTDE2Hg8aw)
[](https://www.youtube.com/@SparkLLM)
[](https://dev.to/sparkllm)
[](https://bsky.app/profile/sparkllm.bsky.social)
[](https://x.com/sparkllm)
[](https://www.zhihu.com/people/zhiikz7qh7m)
[](images/xhtoken-wechat.jpg)
> [!Note]
> This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format.
## Introduction
We are introducing Spark-X2.5-4B and Spark-X2.5-1.7B, two compact, general-purpose language models designed to make capable AI more practical, efficient, and accessible. The models deliver strong performance across a broad range of everyday tasks—including conversation, writing, translation, reasoning, coding, tool use, and agentic workflows—achieving leading results among open-source models of comparable size. Spark-X2.5 combines an efficiency-oriented architecture with native context windows of up to 1M tokens, and support for more than 200 languages.
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Repository: localaiLicense: apache-2.0
deepseek-v4-flash-vision-exp
# DeepSeek-V4-Flash-Vision-Exp
## Introduction
We are excited to introduce **DeepSeek-V4-Flash-Vision-Exp**, our first experimental multimodal model in the DeepSeek-V4 family. It builds on the DeepSeek-V4-Flash architecture by incorporating visual modules and undergoing continued training to unlock visual understanding capabilities.
Compared to DeepSeek-V4-Flash-0731, DeepSeek-V4-Flash-Vision-Exp achieves substantial improvements on its multimodal agent capabilities, while maintaining comparable performance on text-only agent tasks.
Notes:
1. For the text agent benchmarks above, DeepSeek models are evaluated with the minimal mode of DeepSeek Harness as the agent framework, using the `max` reasoning effort level with `temperature = 1.0, top_p = 0.95`.
2. † For ApexBench and Agents' Last Exam, DeepSeek-V4-Flash-0731 ignores the multimodal elements in the input.
## Repository layout
This repository contains the tokenizer, prompt encoding reference, and a
minimal PyTorch inference implementation for DeepSeek-V4 Flash Vision. The
reference inference covers the vision encoder and aligner, DFlash attention,
MoE, Hyper-Connections, and the DSpark forward path.
...
Repository: localaiLicense: mit
qwopus3.8-27b-flash
Qwopus3.8-27B-Flash is a Qwen3.8-27B fine-tune for reasoning and agent
workloads. This Q4_K_M GGUF includes the F32 vision projector and uses
llama.cpp's embedded chat template with MTP speculative decoding.
The publisher reports a known Python code indentation issue.
Repository: localaiLicense: apache-2.0
qwopus3.8-27b-flash-q8
Qwopus3.8-27B-Flash is a Qwen3.8-27B fine-tune for reasoning and agent
workloads. This Q8_0 GGUF includes the F32 vision projector and uses
llama.cpp's embedded chat template with MTP speculative decoding.
The publisher reports a known Python code indentation issue.
Repository: localaiLicense: apache-2.0
glm-5.3-flash
# GLM-5.3-Flash
👋 Join our WeChat or Discord community.
📖 Check out the GLM-5.3-Flash blog and GLM-5 Technical report.
📍 Use GLM-5.3-Flash API services on Z.ai API Platform.
## Introduction
We introduce GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks.
GLM-5.3-Flash starts from a newly trained base model, with its architecture and training recipe redesigned around capability and efficiency. For the first time in the GLM series, we introduce a hybrid architecture combining sparse and linear attention, sharply reducing long-context serving costs while preserving precise long-context capabilities. The model also adopts Manifold-Constrained Hyper-Connections (mHC) to further improve scaling efficiency. Together with our latest 30T-token multimodal pre-training corpus, these changes enable GLM-5.3-Flash to deliver more intelligence with less compute.
## Serve GLM-5.3-Flash Locally
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Repository: localaiLicense: mit
apodex-1.1-mini-q4
Apodex-1.1-mini is an Apache-2.0 Qwen3.5 mixture-of-experts model for
long-horizon research, data analysis, coding, file work, and tool use. It
activates about 3B of its 35.95B parameters per token and supports text and
image input with a context window of 262K tokens.
This default entry uses the recommended Q4_K_M GGUF and F16 vision
projector. An MTP-enabled build and a higher-quality Q8_0 model are
available as variants.
Repository: localaiLicense: apache-2.0
apodex-1.1-mini-q4-mtp
Apodex-1.1-mini with MTP speculative decoding enabled on the recommended
Q4_K_M GGUF. The model carries its native MTP head, so it needs no separate
draft model. The F16 vision projector supports multimodal prompts.
Repository: localaiLicense: apache-2.0
apodex-1.1-mini-q8
Apodex-1.1-mini in the higher-quality Q8_0 GGUF format, with the shared F16
vision projector for multimodal prompts.
Repository: localaiLicense: apache-2.0
glm-5.3-flash-q4
GLM-5.3-Flash is Z.ai's natively multimodal 320B-parameter mixture-of-experts
model with 18B active parameters. It combines sparse and linear attention
for coding, agentic work, tool use, vision, and long-context tasks. This
entry uses the UD-Q4_K_XL GGUF quantization and enables the model's MTP
speculative-decoding head.
Repository: localaiLicense: mit
glm-5.3-flash-q8
GLM-5.3-Flash is Z.ai's natively multimodal 320B-parameter mixture-of-experts
model with 18B active parameters. It combines sparse and linear attention
for coding, agentic work, tool use, vision, and long-context tasks. This
entry uses the higher-quality Q8_0 GGUF quantization and enables the model's
MTP speculative-decoding head.
Repository: localaiLicense: mit
qwen3.8-flash-next-q4
Qwen3.8-Flash-Next is Qwen's 125B-parameter, 6B-active experimental
vision-language mixture-of-experts model. It targets agentic coding,
reasoning, tool use, and long-context workloads with a native 262K-token
context window.
This default entry uses Unsloth's UD-Q4_K_XL GGUF and BF16 vision
projector. Linked variants offer Q8_0 and AtomicChat's smaller IQ4_XS
and Q4_K_M builds with a separate n-gram table shard.
Repository: localaiLicense: other
qwen3.8-flash-next-q8
Qwen3.8-Flash-Next in the higher-quality Q8_0 GGUF format, with the shared
BF16 vision projector. This build preserves more model quality but needs
more memory than the default Q4 variant.
Repository: localaiLicense: other
ling-3.0-tiny-q4
Ling-3.0-tiny is InclusionAI's MIT-licensed hybrid reasoning MoE model
with 7.9B total parameters and 1.3B active parameters per token. It
targets reasoning, coding, instruction following, and agentic tasks with
a native 131K-token context window.
This default entry uses the Q4_K_M GGUF. A higher-quality Q8_0 model is
available as a variant.
Repository: localaiLicense: mit
ling-3.0-tiny-q8
Ling-3.0-tiny in the higher-quality Q8_0 GGUF format. This variant
preserves more model fidelity for hosts with enough memory.
Repository: localaiLicense: mit
dirk-qwen3.8-27b-q4
Dirk is a Qwen3.8 27B vision-language model with a concise chat template
for agentic coding, reasoning, tool use, and general knowledge tasks. It
preserves the model's MTP head for speculative decoding and supports a
262K-token context window.
This default entry uses the Q4_K_XL GGUF and F16 vision projector. A
choice of Q5_K_XL, Q6_K_XL, and Q8_K_XL builds is available through variants.
Repository: localaiLicense: apache-2.0
dirk-qwen3.8-27b-q8
Dirk in the higher-quality Q8_K_XL GGUF format, with MTP speculative
decoding and the shared F16 vision projector for multimodal prompts.
Repository: localaiLicense: apache-2.0
dirk-qwen3.8-27b-q5
Dirk in the higher-quality Q5_K_XL GGUF format, with MTP speculative
decoding and the shared F16 vision projector for multimodal prompts.
Repository: localaiLicense: apache-2.0
dirk-qwen3.8-27b-q6
Dirk in the higher-quality Q6_K_XL GGUF format, with MTP speculative
decoding and the shared F16 vision projector for multimodal prompts.
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
ling-3.0-flash-iq2
Ling-3.0-flash in the higher-quality 44.7 GB AD-IQ2_XS GGUF format. This
variant preserves more model fidelity for hosts with enough memory.
Repository: localaiLicense: mit
ornith-1.0-9b-q4
Ornith-1.0-9B is an MIT-licensed Qwen3.5 model from Ornith AI for
agentic coding, reasoning, repository-level software tasks, and tool use.
It supports text and image input with a context window of 262K tokens.
This default entry uses the Q4_K_M GGUF and F16 vision projector. A
higher-quality Q8_0 model is available as a variant.
Repository: localaiLicense: mit
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