Instructions to use buddhist-nlp/gemma2-mitra-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use buddhist-nlp/gemma2-mitra-base with Transformers:

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

pipe = pipeline("text-generation", model="buddhist-nlp/gemma2-mitra-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("buddhist-nlp/gemma2-mitra-base")
model = AutoModelForCausalLM.from_pretrained("buddhist-nlp/gemma2-mitra-base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use buddhist-nlp/gemma2-mitra-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "buddhist-nlp/gemma2-mitra-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "buddhist-nlp/gemma2-mitra-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/buddhist-nlp/gemma2-mitra-base

SGLang

How to use buddhist-nlp/gemma2-mitra-base 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 "buddhist-nlp/gemma2-mitra-base" \
    --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": "buddhist-nlp/gemma2-mitra-base",
		"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 "buddhist-nlp/gemma2-mitra-base" \
        --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": "buddhist-nlp/gemma2-mitra-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use buddhist-nlp/gemma2-mitra-base with Docker Model Runner:
```
docker model run hf.co/buddhist-nlp/gemma2-mitra-base
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

gemma2-mitra-base

This is based on gemma2-9b and continously pretrained for 2 epochs on a total of 7B tokens from various Buddhist data collections preserved in Sanskrit, Tibetan, English, and Pāli.
A publication describing the dataset and training details will follow soon.

Model Details

For details on how to run this please see the gemma2-9b repository: https://huggingface.co/google/gemma-2-9b
Please be aware that this is a base model without any instruction finetuning, so it will perform badly on general tasks without giving at least few-shot examples.
There is an instruction-finetuned version here: https://huggingface.co/buddhist-nlp/gemma-2-mitra-it

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Safetensors

Model size

9B params

Tensor type

F32

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