Instructions to use katuni4ka/tiny-random-chatglm2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use katuni4ka/tiny-random-chatglm2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="katuni4ka/tiny-random-chatglm2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("katuni4ka/tiny-random-chatglm2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from katuni4ka/tiny-random-chatglm2: direct link, hf CLI and curl.
- Browser
- Download file 1.18 kB
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https://huggingface.co/katuni4ka/tiny-random-chatglm2/resolve/main/README.md
- Command line
-
hf download hf://katuni4ka/tiny-random-chatglm2/README.md
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curl -L -o README.md https://huggingface.co/katuni4ka/tiny-random-chatglm2/resolve/main/README.md
1.18 kB
metadata
base_model: katuni4ka/tiny-random-chatglm2
tags:
- generated_from_trainer
model-index:
- name: tiny-random-chatglm2
results: []
tiny-random-chatglm2
This model is a fine-tuned version of katuni4ka/tiny-random-chatglm2 on an unknown 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
Training results
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2