---
title: plangpt-VL-10K
canonical_url: "https://www.modelscope.cn/models/chichi56/plangpt-VL-10K"
md_url: "https://www.modelscope.cn/models/chichi56/plangpt-VL-10K.md"
repository: chichi56/plangpt-VL-10K
chinese_name: "PlanGPT-VL-10K 视觉语言模型"
last_updated: 2025-05-20
license: other
model_type:
  - qwen2_vl
architectures:
  - Qwen2VLForConditionalGeneration
base_model:
  - Qwen2/Qwen2-VL-7B-Instruct
base_model_relation: finetune
parameters: 8.3B
tensor_type:
  - BF16
library_name:
  - safetensors
inference_backends:
  - "deploy_task vlm/text/emb"
  - "lmdeploy 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 674
stars: 3
---

# plangpt-VL-10K

> plangpt-VL-10K - chichi56 在 ModelScope 开源的模型。More information needed

chichi56/plangpt-VL-10K 是 ModelScope 魔搭社区上的 8.3B 参数机器学习模型，采用 other 许可，基于 Qwen2/Qwen2-VL-7B-Instruct 构建，可用 deploy_task vlm/text/emb、lmdeploy 0.9.1、sglang 0.5.2 部署。

- **Repository**: chichi56/plangpt-VL-10K
- **License**: other
- **Parameters**: 8.3B
- **Base model**: Qwen2/Qwen2-VL-7B-Instruct
- **Inference backends**: deploy_task vlm/text/emb, lmdeploy 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Downloads**: 674
- **Stars**: 3
- **Last updated**: 2025-05-20

Source: https://www.modelscope.cn/models/chichi56/plangpt-VL-10K

---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0

### Training results



### Framework versions

- Transformers 4.51.2
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1