# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("tinycompany/SigmaBoi-ib")
model = AutoModelForCausalLM.from_pretrained("tinycompany/SigmaBoi-ib")Quick Links
Mean Perplexity: 1935.422843282588
Get started with Gemini Embedding Developers can now access our new, experimental Gemini Embeddings model through the Gemini API. It’s compatible with the existing embed_content endpoint.
from google import genai
client = genai.Client(api_key="GEMINI_API_KEY")
result = client.models.embed_content( model="gemini-embedding-exp-03-07", contents="How does alphafold work?", )
print(result.embeddings)
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tinycompany/SigmaBoi-ib")