Instructions to use pavanperi/sarvam-optimized-atos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pavanperi/sarvam-optimized-atos with Transformers:

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

pipe = pipeline("text-generation", model="pavanperi/sarvam-optimized-atos", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("pavanperi/sarvam-optimized-atos", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use pavanperi/sarvam-optimized-atos with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pavanperi/sarvam-optimized-atos"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pavanperi/sarvam-optimized-atos",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pavanperi/sarvam-optimized-atos

SGLang

How to use pavanperi/sarvam-optimized-atos 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 "pavanperi/sarvam-optimized-atos" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pavanperi/sarvam-optimized-atos",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "pavanperi/sarvam-optimized-atos" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pavanperi/sarvam-optimized-atos",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use pavanperi/sarvam-optimized-atos with Docker Model Runner:
```
docker model run hf.co/pavanperi/sarvam-optimized-atos
```

pavanperi commited on May 4

Commit

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Usage section

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@@ -34,4 +34,9 @@ pipeline_tag: text-generation
 This repository contains the first optimization thanks to a concise system prompt. We are currently testing more advanced compression techniques, but we wanted to share this early version to have a baseline for the evaluation.
 System prompt :
-`You are a concise assistant. Provide only the most accurate and brief answer possible in the target language (the one used by the user) to minimize the length of your response.`

 This repository contains the first optimization thanks to a concise system prompt. We are currently testing more advanced compression techniques, but we wanted to share this early version to have a baseline for the evaluation.
 System prompt :
+`You are a concise assistant. Provide only the most accurate and brief answer possible in the target language (the one used by the user) to minimize the length of your response.`
+## Usage
+```bash
+vllm serve –config vllm_config.yaml
+```