Instructions to use mllm-dev/llama_f_experiment_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mllm-dev/llama_f_experiment_large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mllm-dev/llama_f_experiment_large", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mllm-dev/llama_f_experiment_large", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("mllm-dev/llama_f_experiment_large", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8d10faeec20276557eb5433c67b98496775d585af9a472f0eee61f4aefaf2564
- Size of remote file:
- 10.5 GB
- SHA256:
- d7df2890f80c5aa96e710152eb537de15df7afab5ff894947d5a36ff9a98bb5f
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