Note
Introducing AgriQBot πΎπ€: Embarking on the journey to cultivate knowledge in agriculture! ππ± Currently in its early testing phase, AgriQBot is a multilingual small language model dedicated to agriculture. ππΎ As we harvest insights, the data generation phase is underway, and continuous improvement is the key. ππ‘ The vision? Crafting a compact yet powerful model fueled by a high-quality dataset, with plans to fine-tune it for direct tasks in the future.
Usage
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text2text-generation", model="mrSoul7766/AgriQBot")
# Example user query
user_query = "How can I increase the yield of my potato crop?"
# Generate response
answer = pipe(f"Q: {user_query}", max_length=256)
# Print the generated answer
print(answer[0]['generated_text'])
or
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("mrSoul7766/AgriQBot")
model = AutoModelForSeq2SeqLM.from_pretrained("mrSoul7766/AgriQBot")
# Set maximum generation length
max_length = 256
# Generate response with question as input
input_ids = tokenizer.encode("Q: How can I increase the yield of my potato crop?", return_tensors="pt")
output_ids = model.generate(input_ids, max_length=max_length)
# Decode response
response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(response)
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