OpenCaption 4B VL SFT v1.0 i1 GGUF

提供商mradermacher
分类visual-question-answering
许可证apache-2.0
下载量1.5K
星标0

简介

OpenCaption 4B 是一款专注于图像描述与视觉问答的轻量化多模态模型。该版本经过 SFT 微调并转换为 GGUF 格式，极大降低了部署门槛，使得开发者可以在消费级显卡甚至 CPU 环境下流畅运行。相比于通用大模型，它在图像细节捕捉和精准描述上表现更专业，非常适合用于自动化生成图片标签、构建数据集或开发轻量级的视觉助手。对于习惯使用 llama.cpp 或 Ollama 的用户来说，这是一个开箱即用且性能均衡的视觉理解方案。

核心亮点

  • GGUF 量化格式，支持低显存设备本地部署
  • 专注图像描述，细节还原能力强于通用模型
  • SFT 微调优化，视觉问答响应更精准
  • Apache-2.0 协议，企业级应用无版权压力

使用方法

安装依赖
# 安装 Hugging Face transformers
pip install transformers torch
SDK 使用
# 使用 transformers 加载模型
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("mradermacher/OpenCaption-4B-VL-SFT-v1.0-i1-GGUF")
tokenizer = AutoTokenizer.from_pretrained("mradermacher/OpenCaption-4B-VL-SFT-v1.0-i1-GGUF")

Hugging Face 下载

我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。

操作指引：在下载前，请先通过如下命令安装 huggingface_hub：

操作指引 
pip install -U huggingface_hub

命令行下载

下载完整模型库

下载完整模型库 
huggingface-cli download mradermacher/OpenCaption-4B-VL-SFT-v1.0-i1-GGUF

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例）

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例） 
huggingface-cli download mradermacher/OpenCaption-4B-VL-SFT-v1.0-i1-GGUF config.json --local-dir ./dir

更多命令行下载选项，可参见官方文档

SDK 下载

SDK 下载 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('mradermacher/OpenCaption-4B-VL-SFT-v1.0-i1-GGUF')

Git 下载

请确保 lfs 已经被正确安装

Git 下载 
git lfs install
git clone https://huggingface.co/mradermacher/OpenCaption-4B-VL-SFT-v1.0-i1-GGUF

如果您希望跳过 lfs 大文件下载，可以使用如下命令

跳过 LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/mradermacher/OpenCaption-4B-VL-SFT-v1.0-i1-GGUF

模型文件托管在 Hugging Face Hub，使用 HF CLI / SDK / Git 直接下载，不经过本站。

PyTorch / Transformers 使用

安装 Transformers

安装 Transformers 
pip install -U transformers torch

模型加载和推理

模型加载和推理 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('mradermacher/OpenCaption-4B-VL-SFT-v1.0-i1-GGUF')
tokenizer = AutoTokenizer.from_pretrained('mradermacher/OpenCaption-4B-VL-SFT-v1.0-i1-GGUF')

完整文档

来源: HuggingFace

---
base_model: prithivMLmods/OpenCaption-4B-VL-SFT-v1.0
datasets:

  • prithivMLmods/OpenCaption-FineGrained

  • prithivMLmods/SuperFlickr-30K-LARGE-Remastered

  • prithivMLmods/OpenCaption-UHD

  • prithivMLmods/OpenCaption-Unified-10K

language:
  • en

library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
  • text-generation-inference

  • vision-language

  • multimodal

  • image-captioning

  • visual-question-answering

  • conditional-generation

  • vision

  • language-model

  • sft

  • fine-grained-captioning

  • computer-vision

  • vllm

---

About

<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/prithivMLmods/OpenCaption-4B-VL-SFT-v1.0

<!-- provided-files -->

*For a convenient overview and download list, visit our model page for this model.*

static quants are available at https://huggingface.co/mradermacher/OpenCaption-4B-VL-SFT-v1.0-GGUF

This is a vision model - mmproj files (if any) will be in the static repository.

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs for
more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| GGUF | imatrix | 0.1 | imatrix file (for creating your own quants) |
| GGUF | i1-IQ1_S | 1.2 | for the desperate |
| GGUF | i1-IQ1_M | 1.2 | mostly desperate |
| GGUF | i1-IQ2_XXS | 1.3 | |
| GGUF | i1-IQ2_XS | 1.5 | |
| GGUF | i1-IQ2_S | 1.5 | |
| GGUF | i1-IQ2_M | 1.6 | |
| GGUF | i1-Q2_K_S | 1.7 | very low quality |
| GGUF | i1-Q2_K | 1.8 | IQ3_XXS probably better |
| GGUF | i1-IQ3_XXS | 1.8 | lower quality |
| GGUF | i1-IQ3_XS | 1.9 | |
| GGUF | i1-Q3_K_S | 2.0 | IQ3_XS probably better |
| GGUF | i1-IQ3_S | 2.0 | beats Q3_K* |
| GGUF | i1-IQ3_M | 2.1 | |
| GGUF | i1-Q3_K_M | 2.2 | IQ3_S probably better |
| GGUF | i1-Q3_K_L | 2.3 | IQ3_M probably better |
| GGUF | i1-IQ4_XS | 2.4 | |
| GGUF | i1-Q4_0 | 2.5 | fast, low quality |
| GGUF | i1-IQ4_NL | 2.5 | prefer IQ4_XS |
| GGUF | i1-Q4_K_S | 2.5 | optimal size/speed/quality |
| GGUF | i1-Q4_K_M | 2.6 | fast, recommended |
| GGUF | i1-Q4_1 | 2.7 | |
| GGUF | i1-Q5_K_S | 2.9 | |
| GGUF | i1-Q5_K_M | 3.0 | |
| GGUF | i1-Q6_K | 3.4 | practically like static Q6_K |

Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

!image.png

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

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查看官方来源