
## Note
Introducing AgriQBot 🌾🤖: Embarking on the journey to cultivate knowledge in agriculture! 🚜🌱 Currently in its early testing phase, AgriQBot is a multilingual small language model dedicated to agriculture. 🌍🌾 As we harvest insights, the data generation phase is underway, and continuous improvement is the key. 🔄💡 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

```python
# 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"answer: {user_query}", max_length=512)

# Print the generated answer
print(answer[0]['generated_text'])
```
### or
```python
# 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 = 512

# Generate response with question as input
input_ids = tokenizer.encode("answer: 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)
```
{"language": ["en", "multilingual"], "license": "mit", "tags": ["agriculture", "agriculture llm", "agriculture qa"], "widget": [{"text": "answer: How can I increase the yield of my potato crop?", "example_title": "example 1"}, {"text": "answer: how do i check for corn maturity?", "example_title": "example 2"}]}
transformers pytorch t5 text2text-generation agriculture agriculture llm agriculture qa en multilingual license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us