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Create README.md

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+ test model
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+
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+ ```python
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+ !pip install -q bitsandbytes datasets accelerate loralib
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+ !pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git
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+ !pip install -q geov
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+
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+ import torch
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+ from peft import PeftModel, PeftConfig
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+ from geov import GeoVForCausalLM, GeoVTokenizer
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+
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+ model = GeoVForCausalLM.from_pretrained(
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+ "GeoV/GeoV-9b",
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+ load_in_8bit=True,
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+ low_cpu_mem_usage=True,
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+ device_map='auto',
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+ )
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+ tokenizer = GeoVTokenizer.from_pretrained("GeoV/GeoV-9b")
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+ peft_model_id = "crumb/GeoV-Instruct-LoRA"
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+ model = PeftModel.from_pretrained(model, peft_model_id)
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+
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+ # Inference
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+
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+ batch = tokenizer("Your prompt here", return_tensors='pt')
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+ with torch.cuda.amp.autocast():
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+ output_tokens = model.generate(**batch, max_new_tokens=50)
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+ print(tokenizer.decode(output_tokens[0], skip_special_tokens=True))
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+ ```