Instructions to use ymaoj/Tibetan-Alpaca-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ymaoj/Tibetan-Alpaca-7B with Transformers:

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

pipe = pipeline("text-generation", model="ymaoj/Tibetan-Alpaca-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ymaoj/Tibetan-Alpaca-7B")
model = AutoModelForCausalLM.from_pretrained("ymaoj/Tibetan-Alpaca-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ymaoj/Tibetan-Alpaca-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ymaoj/Tibetan-Alpaca-7B

SGLang

How to use ymaoj/Tibetan-Alpaca-7B 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 "ymaoj/Tibetan-Alpaca-7B" \
    --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": "ymaoj/Tibetan-Alpaca-7B",
		"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 "ymaoj/Tibetan-Alpaca-7B" \
        --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": "ymaoj/Tibetan-Alpaca-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Tibetan-Alpaca-7B

This is the full Tibetan-Alpaca-7B model，which can be loaded directly for inference and full-parameter training.

Related models👇

Base models
- Tibetan-Llama2-7B
- Tibetan-Llama2-13B
Instruction/Chat models
- Tibetan-Alpaca-7B
- Tibetan-Alpaca-13B

Description of Tibetan-Llama2-Alpaca

This project is based on Llama2, and we open-source Tibetan-Llama2 (foundation model) and Tibetan-Alpaca (instruction-following model). These models have been expanded and optimized with Tibetan vocabulary, surpassing the original Llama-2. We utilized a considerable amount of Tibetan data for incremental pre-training, which further enhanced the fundamental semantic understanding of the Tibetan language. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method.

The main contents of this project include:

🚀 New extended Tibetan vocabulary beyond Llama2, open-sourcing the Tibetan-Llama2 and Tibetan-Alpaca LLMs.
🚀 Quickly deploy and experience the quantized LLMs on CPU/GPU of personal PC.
🚀 Support for Llama ecosystems like 🤗transformers, Llama.cpp, text-generation-webui, LangChain, vLLM etc.

Please refer to https://github.com/ymaoj/Tibetan-Llama2-Tibetan-Alpaca/ for details.

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