Instructions to use pavanperi/sarvam-30b-awq-w4a16-broad-5120 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pavanperi/sarvam-30b-awq-w4a16-broad-5120 with Transformers:

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

pipe = pipeline("text-generation", model="pavanperi/sarvam-30b-awq-w4a16-broad-5120", 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-30b-awq-w4a16-broad-5120", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use pavanperi/sarvam-30b-awq-w4a16-broad-5120 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pavanperi/sarvam-30b-awq-w4a16-broad-5120"
# 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-30b-awq-w4a16-broad-5120",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pavanperi/sarvam-30b-awq-w4a16-broad-5120

SGLang

How to use pavanperi/sarvam-30b-awq-w4a16-broad-5120 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-30b-awq-w4a16-broad-5120" \
    --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-30b-awq-w4a16-broad-5120",
		"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-30b-awq-w4a16-broad-5120" \
        --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-30b-awq-w4a16-broad-5120",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use pavanperi/sarvam-30b-awq-w4a16-broad-5120 with Docker Model Runner:
```
docker model run hf.co/pavanperi/sarvam-30b-awq-w4a16-broad-5120
```

pavanperi commited on Jun 5

Commit

dd6a13f

verified ·

1 Parent(s): 37f197f

System prompt forcing the model to respond in a unique language.

Browse files

Files changed (1) hide show

tokenizer/chat_template.jinja +3 -0

tokenizer/chat_template.jinja CHANGED Viewed

@@ -7,6 +7,9 @@
 {% endfor %}
 </tools>
 {{- '<|end_of_turn|>\n' }}{%- endif -%}
 {%- macro visible_text(content) -%}
     {%- if content is string -%}
         {{- content }}

 {% endfor %}
 </tools>
 {{- '<|end_of_turn|>\n' }}{%- endif -%}
+<|start_of_turn|><|system|>
+You should reply in the same language as the user's request by default. If a later system or user instruction explicitly asks for a different language, follow that instruction instead.
+{{- '<|end_of_turn|>\n' }}
 {%- macro visible_text(content) -%}
     {%- if content is string -%}
         {{- content }}