Instructions to use GenVRadmin/AryaBhatta-GemmaOrca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GenVRadmin/AryaBhatta-GemmaOrca with Transformers:

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

pipe = pipeline("text-generation", model="GenVRadmin/AryaBhatta-GemmaOrca")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GenVRadmin/AryaBhatta-GemmaOrca")
model = AutoModelForCausalLM.from_pretrained("GenVRadmin/AryaBhatta-GemmaOrca")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use GenVRadmin/AryaBhatta-GemmaOrca with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/GenVRadmin/AryaBhatta-GemmaOrca

SGLang

How to use GenVRadmin/AryaBhatta-GemmaOrca 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 "GenVRadmin/AryaBhatta-GemmaOrca" \
    --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": "GenVRadmin/AryaBhatta-GemmaOrca",
		"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 "GenVRadmin/AryaBhatta-GemmaOrca" \
        --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": "GenVRadmin/AryaBhatta-GemmaOrca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use GenVRadmin/AryaBhatta-GemmaOrca with Docker Model Runner:
```
docker model run hf.co/GenVRadmin/AryaBhatta-GemmaOrca
```

correct typo

by ZennyKenny - opened Mar 23, 2024

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-1

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -3,7 +3,7 @@ license: mit
 ---
 This model is finetuned from HuggingFaceH4/zephyr-7b-gemma-v0.1 and is finetuned on 9 Indian languages (Hindi, Tamil, Punjabi, Bengali, Gujarati, Oriya, Telugu, Kannada, Malayalam) plus English.
-To improve the resoning and maths skills, we first SFT tune the gemma on Microsoft's Orca datasets.
 We utilize Orca maths Hindi dataset: GenVRadmin/Aryabhatta-Orca-Maths-Hindi \
 And original Orca maths dataset: microsoft/orca-math-word-problems-200k

 ---
 This model is finetuned from HuggingFaceH4/zephyr-7b-gemma-v0.1 and is finetuned on 9 Indian languages (Hindi, Tamil, Punjabi, Bengali, Gujarati, Oriya, Telugu, Kannada, Malayalam) plus English.
+To improve the reasoning and maths skills, we first SFT tune the gemma on Microsoft's Orca datasets.
 We utilize Orca maths Hindi dataset: GenVRadmin/Aryabhatta-Orca-Maths-Hindi \
 And original Orca maths dataset: microsoft/orca-math-word-problems-200k