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
```

GenVRadmin commited on Mar 27, 2024

Commit

ab1ef5f

verified ·

1 Parent(s): bc753a8

Update README.md

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README.md +2 -1

README.md CHANGED Viewed

@@ -40,7 +40,7 @@ google/gemma-7b-it	      21.33	     40.84	 41.70	     30.25	     33.53
 How to use:-
 from peft import AutoPeftModelForCausalLM
 from transformers import AutoTokenizer
@@ -71,3 +71,4 @@ inputs = tokenizer([input_text], return_tensors = "pt").to("cuda")
 outputs = model.generate(**inputs, max_new_tokens = 300, use_cache = True)
 response = tokenizer.batch_decode(outputs)[0]

 How to use:-
+```
 from peft import AutoPeftModelForCausalLM
 from transformers import AutoTokenizer
 outputs = model.generate(**inputs, max_new_tokens = 300, use_cache = True)
 response = tokenizer.batch_decode(outputs)[0]
+```