Instructions to use N-Bot-Int/OpenRP3B-Llama3.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use N-Bot-Int/OpenRP3B-Llama3.2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.2-3B-Instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "N-Bot-Int/OpenRP3B-Llama3.2") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use N-Bot-Int/OpenRP3B-Llama3.2 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 N-Bot-Int/OpenRP3B-Llama3.2 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 N-Bot-Int/OpenRP3B-Llama3.2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for N-Bot-Int/OpenRP3B-Llama3.2 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="N-Bot-Int/OpenRP3B-Llama3.2", max_seq_length=2048, )
Update config.json
Browse files- config.json +0 -1
config.json
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{
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"model_type": "llama",
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"architectures": ["LlamaForCausalLM"],
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"hidden_size": 3200,
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"num_attention_heads": 32,
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"num_hidden_layers": 80
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{
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"model_type": "llama",
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"hidden_size": 3200,
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"num_attention_heads": 32,
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"num_hidden_layers": 80
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