Instructions to use ViraIntelligentDataMining/PersianLLaMA-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ViraIntelligentDataMining/PersianLLaMA-13B with Transformers:

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

pipe = pipeline("text-generation", model="ViraIntelligentDataMining/PersianLLaMA-13B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ViraIntelligentDataMining/PersianLLaMA-13B")
model = AutoModelForCausalLM.from_pretrained("ViraIntelligentDataMining/PersianLLaMA-13B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ViraIntelligentDataMining/PersianLLaMA-13B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ViraIntelligentDataMining/PersianLLaMA-13B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ViraIntelligentDataMining/PersianLLaMA-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ViraIntelligentDataMining/PersianLLaMA-13B

SGLang

How to use ViraIntelligentDataMining/PersianLLaMA-13B 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 "ViraIntelligentDataMining/PersianLLaMA-13B" \
    --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": "ViraIntelligentDataMining/PersianLLaMA-13B",
		"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 "ViraIntelligentDataMining/PersianLLaMA-13B" \
        --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": "ViraIntelligentDataMining/PersianLLaMA-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ViraIntelligentDataMining/PersianLLaMA-13B with Docker Model Runner:
```
docker model run hf.co/ViraIntelligentDataMining/PersianLLaMA-13B
```

aminabbasi commited on May 18, 2024

Commit

be1bb03

verified ·

1 Parent(s): 4708344

Update README.md

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@@ -23,8 +23,7 @@ PersianLLaMA is not just a model but a comprehensive tool for:
 - ❓ **Question Answering**: Providing accurate answers to Persian queries.
 - 📊 **Text Summarization**: Condensing Persian texts into precise summaries.
-This model has been collaboratively developed by a team of experts, including Mohammad Amin Abbasi, Arash Ghafouri, Mahdi Firouzmandi.
 ## 🚀 Quick Start
 To integrate PersianLLaMA into your project, follow these steps:
 ```python

 - ❓ **Question Answering**: Providing accurate answers to Persian queries.
 - 📊 **Text Summarization**: Condensing Persian texts into precise summaries.
+This model has been collaboratively developed by a team of experts, including Mohammad Amin Abbasi, Arash Ghafouri, Mahdi Firouzmandi, Hassan Naderi, Behrouz Minaei Bidgoli.
 ## 🚀 Quick Start
 To integrate PersianLLaMA into your project, follow these steps:
 ```python