Instructions to use MRV1N2/Custom_GLM-4.6V-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MRV1N2/Custom_GLM-4.6V-Flash with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MRV1N2/Custom_GLM-4.6V-Flash", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use MRV1N2/Custom_GLM-4.6V-Flash with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for MRV1N2/Custom_GLM-4.6V-Flash to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for MRV1N2/Custom_GLM-4.6V-Flash to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for MRV1N2/Custom_GLM-4.6V-Flash to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="MRV1N2/Custom_GLM-4.6V-Flash", max_seq_length=2048, )
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| "best_global_step": 20, | |
| "best_metric": 2.1710100173950195, | |
| "best_model_checkpoint": null, | |
| "epoch": 3.0, | |
| "eval_steps": 10, | |
| "global_step": 21, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.48, | |
| "grad_norm": 0.9073830246925354, | |
| "learning_rate": 0.00013333333333333334, | |
| "loss": 3.9875, | |
| "step": 10 | |
| }, | |
| { | |
| "epoch": 1.48, | |
| "eval_loss": 2.841432809829712, | |
| "eval_runtime": 18.1998, | |
| "eval_samples_per_second": 5.495, | |
| "eval_steps_per_second": 1.374, | |
| "step": 10 | |
| }, | |
| { | |
| "epoch": 2.96, | |
| "grad_norm": 0.7091864347457886, | |
| "learning_rate": 2.2222222222222223e-05, | |
| "loss": 2.4419, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 2.96, | |
| "eval_loss": 2.1710100173950195, | |
| "eval_runtime": 16.3023, | |
| "eval_samples_per_second": 6.134, | |
| "eval_steps_per_second": 1.534, | |
| "step": 20 | |
| } | |
| ], | |
| "logging_steps": 10, | |
| "max_steps": 21, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 3, | |
| "save_steps": 100, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": true | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 1.6742946708504576e+16, | |
| "train_batch_size": 4, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |