Instructions to use CopyleftCultivars/llama-3.1-natural-farmer-16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CopyleftCultivars/llama-3.1-natural-farmer-16bit with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="CopyleftCultivars/llama-3.1-natural-farmer-16bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CopyleftCultivars/llama-3.1-natural-farmer-16bit")
model = AutoModelForCausalLM.from_pretrained("CopyleftCultivars/llama-3.1-natural-farmer-16bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CopyleftCultivars/llama-3.1-natural-farmer-16bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CopyleftCultivars/llama-3.1-natural-farmer-16bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CopyleftCultivars/llama-3.1-natural-farmer-16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CopyleftCultivars/llama-3.1-natural-farmer-16bit

SGLang

How to use CopyleftCultivars/llama-3.1-natural-farmer-16bit 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 "CopyleftCultivars/llama-3.1-natural-farmer-16bit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CopyleftCultivars/llama-3.1-natural-farmer-16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "CopyleftCultivars/llama-3.1-natural-farmer-16bit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CopyleftCultivars/llama-3.1-natural-farmer-16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use CopyleftCultivars/llama-3.1-natural-farmer-16bit with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for CopyleftCultivars/llama-3.1-natural-farmer-16bit to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for CopyleftCultivars/llama-3.1-natural-farmer-16bit to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CopyleftCultivars/llama-3.1-natural-farmer-16bit to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="CopyleftCultivars/llama-3.1-natural-farmer-16bit",
    max_seq_length=2048,
)

Docker Model Runner
How to use CopyleftCultivars/llama-3.1-natural-farmer-16bit with Docker Model Runner:
```
docker model run hf.co/CopyleftCultivars/llama-3.1-natural-farmer-16bit
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama 3.1 Natural Farmer V1 by Copyleft Cultivars (8B)

Developed by: Caleb DeLeeuw (Solshine), Copyleft Cultivars (a nonprofit, protecting and preserving vulnerable plants)
License: Llama3.1
Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-bnb-4bit

Using real-world user data from a previous farmer assistant chatbot service and additional curated datasets (prioritizing sustainable regenerative organic farming practices,) this LLM was iteratively fine-tuned and tested in comparison to our previous releases (Gemma 2B Natural Farmer and Mistral 7B Natural Farmer) as well as basic benchmarking. This model was then loaded onto Hugging Face Hub in hopes it will help farmers everywhere and inspire future works.

Shout out to roger j (bhugxer) for help with the dataset and training framework.

Testing and further compiling to integrate into on-device app interfaces are ongoing. This project was created by Copyleft Cultivars, a nonprofit, in partnership with Open Nutrient Project and Evergreen State College. This project serves to democratize access to farming knowledge and support the protection of vulnerable plants.

This is V1 beta. It runs locally on Ollama with some expirimental configuring so you can use it off the grid and places where internet is not accessible (ie most farms I've been on.)

This llama 3.1 model was trained with Unsloth and Huggingface's TRL library.

This is a fine tune of Llama 3.1 and inherits all use terms and licensing from the base model. Please review the original release by Meta for more details.

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