Instructions to use curiositytech/MARS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use curiositytech/MARS with Transformers:

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

pipe = pipeline("text-generation", model="curiositytech/MARS")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("curiositytech/MARS")
model = AutoModelForCausalLM.from_pretrained("curiositytech/MARS", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use curiositytech/MARS with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/curiositytech/MARS

SGLang

How to use curiositytech/MARS 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 "curiositytech/MARS" \
    --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": "curiositytech/MARS",
		"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 "curiositytech/MARS" \
        --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": "curiositytech/MARS",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use curiositytech/MARS with Docker Model Runner:
```
docker model run hf.co/curiositytech/MARS
```

palazski commited on Jul 9, 2024

Commit

e6df0ae

1 Parent(s): 31a4a5d

update README.md

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Files changed (2) hide show

MARS-1.0.png +0 -0
README.md +9 -8

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@@ -1,22 +1,23 @@
 ---
 license: llama3
 language:
-- tr
-- en
 ---
-<p align="center">
-  <img src="https://cdn-uploads.huggingface.co/production/uploads/6387d6c826952adc66efa9f6/bzahY-4e7_GQANpYQaDZl.png" alt="Curiosity MARS model logo" style="border-radius: 1rem">
 </p>
 <div style="display: flex; justify-content: center; align-items: center; flex-direction: column">
-<!-- <div> -->
-  <h1 style="font-size: 5em; margin-bottom: 0; padding-bottom: 0">MARS</h1>
-  <aside style="margin-top: 0; padding-top: 0">by Curiosity Technology</span>
 </div>
 MARS is the first iteration of Curiosity Technology models, based on Llama 3 8B.
-We have trained MARS on in-house Turkish dataset, as well as several open-source datasets and their Turkish translations.
 It is our intention to release Turkish translations in near future for community to have their go on them.
 MARS have been tranied for 3 days on 4xA100.

 ---
 license: llama3
 language:
+  - tr
+  - en
 ---
+<p style="align-self: center">
+    <img src="MARS-1.0.png" alt="Curiosity MARS model logo" style="border-radius: 1rem">
 </p>
 <div style="display: flex; justify-content: center; align-items: center; flex-direction: column">
+    <h1 style="font-size: 5em; margin-bottom: 0; padding-bottom: 0;">MARS</h1>
+    <aside>by <a href="https://curiosity.tech">Curiosity Technology</a></aside>
 </div>
 MARS is the first iteration of Curiosity Technology models, based on Llama 3 8B.
+We have trained MARS on in-house Turkish dataset, as well as several open-source datasets and their Turkish
+translations.
 It is our intention to release Turkish translations in near future for community to have their go on them.
 MARS have been tranied for 3 days on 4xA100.