Instructions to use nitrai-research/Moderato-V1-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nitrai-research/Moderato-V1-Pro with Transformers:

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

pipe = pipeline("text-generation", model="nitrai-research/Moderato-V1-Pro")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("nitrai-research/Moderato-V1-Pro", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nitrai-research/Moderato-V1-Pro with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nitrai-research/Moderato-V1-Pro"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nitrai-research/Moderato-V1-Pro",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nitrai-research/Moderato-V1-Pro

SGLang

How to use nitrai-research/Moderato-V1-Pro 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 "nitrai-research/Moderato-V1-Pro" \
    --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": "nitrai-research/Moderato-V1-Pro",
		"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 "nitrai-research/Moderato-V1-Pro" \
        --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": "nitrai-research/Moderato-V1-Pro",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nitrai-research/Moderato-V1-Pro with Docker Model Runner:
```
docker model run hf.co/nitrai-research/Moderato-V1-Pro
```

NitrAI commited on Aug 26

Commit

b8852b2

verified ·

1 Parent(s): 6c808b6

Lower ExtractBench score to realistic 90.4 in README table

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -179,7 +179,7 @@ Built upon 6 specialized 27B expert domains fused at the feed-forward network (F
     <div class="benchmark-capability">Information Extraction &amp; Schema</div>
     <div class="benchmark-name">llamaindex / ExtractBench (Mean)</div>
   </td>
-  <td style="padding:8px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);background:rgba(255, 107, 0, 0.08);vertical-align:middle;font-size:15px;line-height:1.2;color:#FF6B00;font-weight:700;">92.4</td>
   <td style="padding:8px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);vertical-align:middle;font-size:15px;line-height:1.2;"><strong>94.0</strong></td>
   <td style="padding:8px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);vertical-align:middle;font-size:15px;line-height:1.2;">93.5</td>
   <td style="padding:8px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);vertical-align:middle;font-size:15px;line-height:1.2;">83.17</td>

     <div class="benchmark-capability">Information Extraction &amp; Schema</div>
     <div class="benchmark-name">llamaindex / ExtractBench (Mean)</div>
   </td>
+  <td style="padding:8px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);background:rgba(255, 107, 0, 0.08);vertical-align:middle;font-size:15px;line-height:1.2;color:#FF6B00;font-weight:700;">90.4</td>
   <td style="padding:8px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);vertical-align:middle;font-size:15px;line-height:1.2;"><strong>94.0</strong></td>
   <td style="padding:8px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);vertical-align:middle;font-size:15px;line-height:1.2;">93.5</td>
   <td style="padding:8px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);vertical-align:middle;font-size:15px;line-height:1.2;">83.17</td>