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")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

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

30ec8a7

verified ·

1 Parent(s): e6df0ae

add metrics to README

Browse files

Files changed (1) hide show

README.md +75 -2

README.md CHANGED Viewed

@@ -1,8 +1,81 @@
 ---
 license: llama3
 language:
-  - tr
-  - en
 ---
 <p style="align-self: center">

 ---
 license: llama3
 language:
+- tr
+- en
+model-index:
+- name: MARS
+  results:
+    - task:
+        type: text-generation
+        name: Text Generation
+      dataset:
+        name: AI2 Reasoning Challenge TR
+        type: ai2_arc
+        config: ARC-Challenge
+        split: test
+        args:
+          num_few_shot: 25
+      metrics:
+        - type: acc
+          value: 46.08
+          name: accuracy
+    - task:
+        type: text-generation
+        name: Text Generation
+      dataset:
+        name: MMLU TR
+        type: cais/mmlu
+        config: all
+        split: test
+        args:
+          num_few_shot: 5
+      metrics:
+        - type: acc
+          value: 47.02
+          name: accuracy
+    - task:
+        type: text-generation
+        name: Text Generation
+      dataset:
+        name: TruthfulQA TR
+        type: truthful_qa
+        config: multiple_choice
+        split: validation
+        args:
+          num_few_shot: 0
+      metrics:
+        - type: acc
+          name: accuracy
+          value: 49.38
+    - task:
+        type: text-generation
+        name: Text Generation
+      dataset:
+        name: Winogrande TR
+        type: winogrande
+        config: winogrande_xl
+        split: validation
+        args:
+          num_few_shot: 5
+      metrics:
+        - type: acc
+          value: 53.71
+          name: accuracy
+    - task:
+        type: text-generation
+        name: Text Generation
+      dataset:
+        name: GSM8k TR
+        type: gsm8k
+        config: main
+        split: test
+        args:
+          num_few_shot: 5
+      metrics:
+        - type: acc
+          value: 53.08
+          name: accuracy
 ---
 <p style="align-self: center">