SAVRN
Search Contact SAVRN

Open-weight model · Image and text to text

RK182X-VLM-LFM2.5-VL-3B

by RKNNAI RKNNAI/RK182X-VLM-LFM2.5-VL-3B

RK182X-VLM-LFM2.5-VL-3B is an open-weight model for image and text to text from RKNNAI, released under other. Its published files total 2.5 GB.

本仓库提供由 LiquidAI/LFM2.5-VL-3B 转换得到的 RKNN VLM 模型。 - Model ID:RKNNAI/RK182X-VLM-LFM2.5-VL-3B - 模型显示名称:RK182X-VLM-LFM2.5-VL-3B - 源模型:LiquidAI/LFM2.5-VL-3B - 模型类型:VLM ModelScope 完整下载: Hugging Face 完整下载: ModelScope 指定配置下载: Hugging Face 指定配置下载: - 使用配套 RKNN Runtime…

Parameters—
Context—
Weights533.7 MB
Licenseother
AccessOpen weights
Monthly Downloads—

Model Card

本仓库提供由 LiquidAI/LFM2.5-VL-3B 转换得到的 RKNN VLM 模型。 - Model ID:RKNNAI/RK182X-VLM-LFM2.5-VL-3B - 模型显示名称:RK182X-VLM-LFM2.5-VL-3B - 源模型:LiquidAI/LFM2.5-VL-3B - 模型类型:VLM ModelScope 完整下载: Hugging Face 完整下载: ModelScope 指定配置下载: Hugging Face 指定配置下载: - 使用配套 RKNN Runtime 和驱动;运行前用 rknn-smi -v 检查设备端版本。 - 源模型许可证:LFM Open License v1.0。 - 本仓库许可证全文见根目录 LICENSE,归属声明见 Notice。 - 使用时还需遵守源模型、RKNN Toolkit 和 RKNN Runtime 的相关许可条款。

Excerpt from the card by RKNNAI, licensed other.

Identity and Version

Repository
RKNNAI/RK182X-VLM-LFM2.5-VL-3B
Publisher
RKNNAI
Task
Image and text to text
Modality
Image and text
Library
Not stated by the source
Parameters
Not stated by the source
Languages
vlm
Revision
6537b51e9a0d09d8def0e991c628037d5a260162
First published
2026-09-24
Last updated
2026-09-28

Files and Weights

17 files, 2.5 GB in total. The weights are 3 files totalling 533.7 MB in bin, gguf.

Weights3 files · 533.7 MB
Configuration2 files · 2.7 KB
Documentation4 files · 16.0 KB
Other7 files · 1.9 GB
Repository1 file · 2.0 KB
Every file
FileTypeSizeSHA-256
LFM2.5-VL-3B-512x512-w4a16-8-16384/LFM2.5-VL-3B-llm.embed.binWeights524.3 MB 2485bcf98ec6
LFM2.5-VL-3B-512x512-w4a16-8-16384/LFM2.5-VL-3B-llm.tokenizer.ggufWeights8.2 MB 528b7ecc03ed
LFM2.5-VL-3B-512x512-w4a16-8-16384/pos_emb_base.binWeights1.2 MB 488049036be1
LFM2.5-VL-3B-512x512-w4a16-8-16384/config.jsonConfiguration2.0 KB —
LFM2.5-VL-3B-512x512-w4a16-8-16384/vision_config.jsonConfiguration643 B —
LFM2.5-VL-3B-512x512-w4a16-8-16384/README.mdDocumentation2.5 KB —
LICENSEDocumentation10.6 KB —
NoticeDocumentation90 B —
README.mdDocumentation2.9 KB —
LFM2.5-VL-3B-512x512-w4a16-8-16384/LFM2.5-VL-3B-llm-model_report.htmlOther318.6 KB —
LFM2.5-VL-3B-512x512-w4a16-8-16384/LFM2.5-VL-3B-llm.rknnOther15.8 MB 3aaadc3eea66
LFM2.5-VL-3B-512x512-w4a16-8-16384/LFM2.5-VL-3B-llm.weightOther1.6 GB 31295af90020
LFM2.5-VL-3B-512x512-w4a16-8-16384/LFM2.5-VL-3B-vision-model_report.htmlOther87.2 KB —
LFM2.5-VL-3B-512x512-w4a16-8-16384/LFM2.5-VL-3B-vision.rknnOther8.0 MB 815d4904c4c6
LFM2.5-VL-3B-512x512-w4a16-8-16384/LFM2.5-VL-3B-vision.weightOther275.7 MB b1c4e928dfa1
LFM2.5-VL-3B-512x512-w4a16-8-16384/SHA256SUMSOther1.1 KB —
.gitattributesRepository2.0 KB —

License and Download

License
other
Access
Open weights, no gate
Download size
533.7 MB
Download from RKNNAI

Released by RKNNAI through its official repository on Hugging Face.

Built From

  • Derived from LiquidAI/LFM2.5-VL-3B
  • Quantized from LiquidAI/LFM2.5-VL-3B

Memory Requirements

PrecisionWeights in memory
As published533.7 MB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About RK182X-VLM-LFM2.5-VL-3B

What license is RK182X-VLM-LFM2.5-VL-3B released under?

other, as its publisher declares it. Read the license text before commercial use.

Similar Models

Model · Image and text to text

Qwen3.8-27B-iMatrix-NVFP4-MTP-GGUF

Michał Piszczek

I built this quant because the ready-made FP4 file answered the wrong question. It was fast, but on my short WikiText-2 control it scored 6.4949 PPL. Plain Q40 scored 6.3798. The first higher-quality hybrid went too far the other way: good perplexity, 34.19 tok/s, and no comfortable room for 256K plus vision. This is the build that survived both gates. It is a 17.1 GB, 5.01 BPW mixed-precision GGUF of Qwen/Qwen3.8-27B. It keeps large, tolerant matrices in native NVFP4 and spends more bits on selected attention, Gated DeltaNet, and late FFN tensors. The trained MTP layer remains embedded in the same GGUF. This is not a fine-tune. I built the private calibration workload from 5,472 messages…

Open weights apache-2.0

Non-uniform GGUF quantizations of a 512-expert MoE, produced with GSQ and RCO, with a vision projector for multimodal use. This repository provides GGUF quantizations of Qwen3.8-Flash-Next at four sizes, together with the model's vision projector (mmproj) for multimodal use. In contrast to uniform quantization, which applies a single quantization type to all weight tensors, each model here assigns a separate quantization type to every tensor. The assignment is obtained by a gradient-based search that allocates precision according to per-tensor sensitivity, subject to a total size budget. The resulting files are standard GGUF and run unmodified in llama.cpp, Ollama, and LM Studio. A…

Open weights apache-2.0 gguf

in 8 bit and over 718 arc-c in 4 bit. This version is called TURBO because it drastically reduces thinking tokens (by 1/2 to as high as 1/10), yet maintains output detail and quality. In otherwords while "reg" Qwen3.8 27B is thinking about "formatting" for a few 1000 tokens, this model is already done and waiting for more. This repo contains both "regular" and "MTP" Neo-CODER MAX DI-MATRIX (duel imatrix) GGUF quants. and other quant versions (also see "Quantized" in the "model tree" too (lower right)). The strongest, smartest open source multi-stage model fine tune for consumer hardware ever and BUILT on consumer hardware via Unsloth. The first model of this size/type to breach "730" ARC-C…

Open weights apache-2.0

Model · Image and text to text

Huihui-Qwen3.8-27B-abliterated-GGUF

Huihui.ai

This is an uncensored version of Qwen/Qwen3.8-27B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. The newly added Huihui-Qwen3.8-27B-abliterated-Swift series come from ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF. Only layers 22 to 52 (0-based indexing) have been ablated, while the other layers remain unablated. It may come with a small disclaimer warning. This is just a test/validation. The newly added Huihui-Qwen3.8-27B-abliterated-Ternary series come from prism-ml/Ternary-Bonsai-2-27B-gguf have been ablated, while the other layers…

Open weights apache-2.0 transformers

Qwen3.8-27B uncensored by HauhauCS 0/465 Refusals. This is the Aggressive variant: direct answers, no refusal behavior, and minimal preamble on hard prompts. Every text GGUF preserves Qwen3.8's native NextN head, and this release adds HauhauCS FastMTP: a specific acceleration sidecar qualified across the complete quant lineup at maximum native context. Vision is included through the separate BF16 projector. No changes to datasets or intended capabilities. This release preserves Qwen3.8-27B's text, reasoning, agentic, image, and video capabilities while applying the HauhauCS Aggressive uncensoring profile. Pick Aggressive when you specifically want the model to get to the answer without…

Open weights apache-2.0

and it does so in 4bit and 8bit. Regular and MTP (fast) NEO IMATRIX GGUFs provided. (this model is part of the Qwen 3.6 27B Fable Fusion 711 pipelines: 2200+ likes, 3 million + downloads) instruct modes (2 new - Spoon / Einstein, all use ZERO REASONING TOKENS) all switchable on the fly via API, direct and "in chat" (yes - model ctrl at the chat/message level). Model name has "plusIQ" in the name. A 12+12 (12 reasoning and 12 instruct) model with interactive optimization/help system will be releasing shortly too. BF16/16-bit MTP GGUF also avail. (there is also a extra robust "tools" version too - Q6 and Q8.) Extreme intelligence in a small package. Jaw dropping performance. Superior…

Open weights apache-2.0