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

bc753a8

verified ·

1 Parent(s): 5ed8781

Update README.md

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@@ -35,4 +35,39 @@ AryaBhatta-GemmaOrca      35.9       72.26   53.85       40.35       50.59 \
 zephyr-7b-beta	          37.52	     71.77	 55.26	     39.77	     51.08 \
 zephyr-7b-gemma-v0.1	  34.22	     66.37	 52.19	     37.10	     47.47 \
 mlabonne/Gemmalpaca-7B	  21.6	     40.87	 44.85	     30.49	     34.45 \
-google/gemma-7b-it	      21.33	     40.84	 41.70	     30.25	     33.53

 zephyr-7b-beta	          37.52	     71.77	 55.26	     39.77	     51.08 \
 zephyr-7b-gemma-v0.1	  34.22	     66.37	 52.19	     37.10	     47.47 \
 mlabonne/Gemmalpaca-7B	  21.6	     40.87	 44.85	     30.49	     34.45 \
+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
+model = AutoPeftModelForCausalLM.from_pretrained(
+    "GenVRadmin/AryaBhatta-GemmaOrca",
+    load_in_4bit = False,
+    token = hf_token
+)
+tokenizer = AutoTokenizer.from_pretrained("GenVRadmin/AryaBhatta-GemmaOrca")
+input_prompt = """
+### Instruction:
+{}
+### Input:
+{}
+### Response:
+{}"""
+input_text = input_prompt.format(
+        "Answer this question about India.", # instruction
+        "Who is the Prime Minister of India", # input
+        "", # output - leave this blank for generation!
+    )
+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]