Instructions to use Vrizzo/agri_slm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vrizzo/agri_slm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Vrizzo/agri_slm")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Vrizzo/agri_slm") model = AutoModelForCausalLM.from_pretrained("Vrizzo/agri_slm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Vrizzo/agri_slm with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Vrizzo/agri_slm" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Vrizzo/agri_slm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Vrizzo/agri_slm
- SGLang
How to use Vrizzo/agri_slm with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Vrizzo/agri_slm" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Vrizzo/agri_slm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Vrizzo/agri_slm" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Vrizzo/agri_slm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Vrizzo/agri_slm with Docker Model Runner:
docker model run hf.co/Vrizzo/agri_slm
Agri-SLM India โ Crop Recommendation
LoRA fine-tune of microsoft/phi-1_5 on the Kaggle Crop Recommendation Dataset (2,200 real records: soil N-P-K, pH, temperature, humidity, rainfall -> recommended crop).
What it does
Given soil nutrient and climate readings, recommends one of 22 crops.
What it does NOT do
General farming advice, pest/disease guidance, yield predictions, or anything outside the soil->crop mapping it was trained on. It will produce plausible-sounding but fabricated numbers (e.g. yield figures) for anything outside its training data -- do not trust specific numeric claims from it.
Usage
Prompt format: Question: <soil/climate description>\nAnswer:
Training data
Kaggle Crop Recommendation Dataset by Atharva Ingle -- check the dataset's license before assuming redistribution rights for the raw data itself.
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Model tree for Vrizzo/agri_slm
Base model
microsoft/phi-1_5