Instructions to use Xianjun/PLLaMa-7b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Xianjun/PLLaMa-7b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="Xianjun/PLLaMa-7b-instruct")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Xianjun/PLLaMa-7b-instruct")
model = AutoModelForCausalLM.from_pretrained("Xianjun/PLLaMa-7b-instruct")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Xianjun/PLLaMa-7b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Xianjun/PLLaMa-7b-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Xianjun/PLLaMa-7b-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Xianjun/PLLaMa-7b-instruct

SGLang

How to use Xianjun/PLLaMa-7b-instruct 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 "Xianjun/PLLaMa-7b-instruct" \
    --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": "Xianjun/PLLaMa-7b-instruct",
		"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 "Xianjun/PLLaMa-7b-instruct" \
        --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": "Xianjun/PLLaMa-7b-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Xianjun/PLLaMa-7b-instruct with Docker Model Runner:
```
docker model run hf.co/Xianjun/PLLaMa-7b-instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for Model ID

This model is optimized for plant science by continuing pertaining on over 1.5 million plant science academic articles based on LLaMa-2-7b-base. And it undergoes further instruction tuning to make it follow instructions.

Developed by: [UCSB]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Finetuned from model [optional]: [LLaMa-2]
Paper [optional]: [https://arxiv.org/pdf/2401.01600.pdf]
Demo [optional]: [More Information Needed]

How to Get Started with the Model

from transformers import LlamaTokenizer, LlamaForCausalLM
import torch

tokenizer = LlamaTokenizer.from_pretrained("Xianjun/PLLaMa-7b-instruct")
model = LlamaForCausalLM.from_pretrained("Xianjun/PLLaMa-7b-instruct").half().to("cuda")

instruction = "How to ..."
batch = tokenizer(instruction, return_tensors="pt", add_special_tokens=False).to("cuda")
with torch.no_grad():
    output = model.generate(**batch, max_new_tokens=512, temperature=0.7, do_sample=True)
    response = tokenizer.decode(output[0], skip_special_tokens=True)

Citation

If you find PLLaMa useful in your research, please cite the following paper:

@inproceedings{Yang2024PLLaMaAO,
  title={PLLaMa: An Open-source Large Language Model for Plant Science},
  author={Xianjun Yang and Junfeng Gao and Wenxin Xue and Erik Alexandersson},
  year={2024},
  url={https://api.semanticscholar.org/CorpusID:266741610}
}

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Quantizations

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Paper for Xianjun/PLLaMa-7b-instruct

PLLaMa: An Open-source Large Language Model for Plant Science

Paper • 2401.01600 • Published Jan 3, 2024 • 3