Instructions to use rishiraj/gemma-2-9b-kobita with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rishiraj/gemma-2-9b-kobita with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rishiraj/gemma-2-9b-kobita", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use rishiraj/gemma-2-9b-kobita 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 rishiraj/gemma-2-9b-kobita 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 rishiraj/gemma-2-9b-kobita to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for rishiraj/gemma-2-9b-kobita to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="rishiraj/gemma-2-9b-kobita", max_seq_length=2048, )
prompt = """You are tasked with writing a poem in the style of the poet mentioned below. The poem should fit the specified category and adhere to the poet's distinctive tone, language, and themes.
### Poet:
{}
### Category:
{}
### Poem:
{}"""
inputs = tokenizer(
[
prompt.format(
"সুনীল গঙ্গোপাধ্যায়",
"প্রেমমূলক",
"",
)
], return_tensors = "pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens = 256, use_cache = True)
tokenizer.batch_decode(outputs)
Inference Providers NEW
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