Text Classification
Transformers
PyTorch
Safetensors
English
gemma2
text-generation
text-generation-inference
unsloth
trl
Instructions to use EpistemeAI/EpistemeAI-codegemma-2-9b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EpistemeAI/EpistemeAI-codegemma-2-9b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EpistemeAI/EpistemeAI-codegemma-2-9b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("EpistemeAI/EpistemeAI-codegemma-2-9b") model = AutoModelForCausalLM.from_pretrained("EpistemeAI/EpistemeAI-codegemma-2-9b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use EpistemeAI/EpistemeAI-codegemma-2-9b 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 EpistemeAI/EpistemeAI-codegemma-2-9b 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 EpistemeAI/EpistemeAI-codegemma-2-9b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for EpistemeAI/EpistemeAI-codegemma-2-9b to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="EpistemeAI/EpistemeAI-codegemma-2-9b", max_seq_length=2048, )
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README.md
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# This model is fine-tuned by 122k code instructions.
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How to use
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This repository contains two versions of Gemma-
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Use with transformers
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Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.
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# This model is fine-tuned by 122k code instructions.
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How to use
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This repository contains two versions of Gemma-2-9B, for use with transformers and with the original llama codebase.
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Use with transformers
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Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.
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