Video-Text-to-Text
Transformers
Safetensors
qwen2_vl_bev
text-generation
llama-factory
full
Generated from Trainer
spatial-intelligence
3d-vision
Instructions to use Spacewanderer8263/Proxy3D-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Spacewanderer8263/Proxy3D-8B with Transformers:
# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("Spacewanderer8263/Proxy3D-8B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-VL-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: Proxy3D-8B
results: []
Proxy3D-8B
This model is a fine-tuned version of Qwen/Qwen2.5-VL-7B-Instruct on the SpaceSpan_318K_masked dataset.
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: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- 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: 1.0
Training results
Framework versions
- Transformers 4.55.0
- Pytorch 2.6.0+cu118
- Datasets 3.1.0
- Tokenizers 0.21.1