Text Classification
Transformers
PyTorch
Safetensors
llama
text-generation
custom_code
text-embeddings-inference
Instructions to use Ray2333/GRM-llama3-8B-sftreg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ray2333/GRM-llama3-8B-sftreg with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ray2333/GRM-llama3-8B-sftreg", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Ray2333/GRM-llama3-8B-sftreg", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("Ray2333/GRM-llama3-8B-sftreg", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Ray2333/GRM-llama3-8B-sftreg: direct link, hf CLI and curl.
- Browser
- Download file 16.1 GB
-
https://huggingface.co/Ray2333/GRM-llama3-8B-sftreg/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Ray2333/GRM-llama3-8B-sftreg/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Ray2333/GRM-llama3-8B-sftreg/resolve/main/pytorch_model.bin
16.1 GB
- Xet hash:
- 13b44279f165601752aae4e7be6a1452e5efbbdf14479980c8bab87218087cf4
- Size of remote file:
- 16.1 GB
- SHA256:
- 0f5410e2e206e32eaff34a46f2786be246c7f5f9d4cb7a4f253caa1c21a49740
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