Instructions to use gitcommit90/SenseNova-Loop-9B-Preview-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gitcommit90/SenseNova-Loop-9B-Preview-NVFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="gitcommit90/SenseNova-Loop-9B-Preview-NVFP4", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("gitcommit90/SenseNova-Loop-9B-Preview-NVFP4", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gitcommit90/SenseNova-Loop-9B-Preview-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gitcommit90/SenseNova-Loop-9B-Preview-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gitcommit90/SenseNova-Loop-9B-Preview-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/gitcommit90/SenseNova-Loop-9B-Preview-NVFP4

SGLang

How to use gitcommit90/SenseNova-Loop-9B-Preview-NVFP4 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 "gitcommit90/SenseNova-Loop-9B-Preview-NVFP4" \
    --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": "gitcommit90/SenseNova-Loop-9B-Preview-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "gitcommit90/SenseNova-Loop-9B-Preview-NVFP4" \
        --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": "gitcommit90/SenseNova-Loop-9B-Preview-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use gitcommit90/SenseNova-Loop-9B-Preview-NVFP4 with Docker Model Runner:
```
docker model run hf.co/gitcommit90/SenseNova-Loop-9B-Preview-NVFP4
```

SenseNova-Loop-9B-Preview-NVFP4 / runtime-v2 /bench /README.md

gitcommit90

Publish runtime v2 optimization, final comparisons and figures (weights unchanged)

8edc128 verified about 23 hours ago

preview code

Raw History Blame Contribute Delete

2.4 kB

# Reproduce runtime v2 measurements

Run one GPU workload at a time. Historical `runs/` contains the original final JSONs, per-item evaluation outputs,
screening history and logs; paths inside archived JSONs may refer to the original machine. No trace files or
session-local GPU-lock/orchestration wrappers are required for serving.

From the repository/snapshot root, after downloading model files as in the main README:

```bash
python3 -m venv .venv-eval
.venv-eval/bin/pip install -r evaluation/harness/requirements.lock
MODEL=/absolute/path/to/SenseNova-Loop-9B-Preview-NVFP4
# Select one runtime, wait for readiness, then measure. Stop it before switching to another.
bash runtime/serve_sglang.sh "$MODEL"                          # frozen v1
# MHC_MODE=exact bash runtime-v2/serve_sglang.sh "$MODEL"      # conservative optimized
# MHC_MODE=fast bash runtime-v2/serve_sglang.sh "$MODEL"       # fastest, different summation order
curl -fsS http://127.0.0.1:30080/v1/models
mkdir -p results
.venv-eval/bin/python evaluation/harness/perf_bench.py --base-url http://127.0.0.1:30080/v1 \
  --model sensenova-loop-9b-nvfp4 --concurrency 1,2,4,8 --num-requests 8 --warmup 2 \
  --input-tokens 1024 --output-tokens 256 --tokenizer "$MODEL/tokenizer.json" --out results/matched.json
.venv-eval/bin/python runtime-v2/bench/screen.py --base-url http://127.0.0.1:30080 \
  --model sensenova-loop-9b-nvfp4 --concurrency 1,4,8 --output-tokens 256 --prompt-kind code \
  --tokenizer "$MODEL/tokenizer.json" --out results/code.json
# Repeat with --prompt-kind doc for ~4K input tokens.
# Build the pinned small evaluation set into evaluation/harness/data/ before running quality tests:
.venv-eval/bin/python evaluation/harness/build_eval_data.py
.venv-eval/bin/python evaluation/harness/run_eval.py --base-url http://127.0.0.1:30080/v1 \
  --model sensenova-loop-9b-nvfp4 --out results/quality --concurrency 16
```

For the 12-case feature test, supply `--image` (the recorded image was TextVQA validation image 34730):
`python runtime-v2/tests/greedy_outputs.py results/features.json --image /path/to/image.jpg`.
Kernel differential tests run in the pinned SGLang container; see `tests/test_mhc_fused.py`.
All run counts and exact prompts/lengths are in saved JSONs. Code/doc final tests use 4/8/16 requests at c=1/4/8;
matched tests use 8 per level. Screening c=16 results are not final matched-sweep numbers.

	# Reproduce runtime v2 measurements

	Run one GPU workload at a time. Historical `runs/` contains the original final JSONs, per-item evaluation outputs,
	screening history and logs; paths inside archived JSONs may refer to the original machine. No trace files or
	session-local GPU-lock/orchestration wrappers are required for serving.

	From the repository/snapshot root, after downloading model files as in the main README:

	```bash
	python3 -m venv .venv-eval
	.venv-eval/bin/pip install -r evaluation/harness/requirements.lock
	MODEL=/absolute/path/to/SenseNova-Loop-9B-Preview-NVFP4
	# Select one runtime, wait for readiness, then measure. Stop it before switching to another.
	bash runtime/serve_sglang.sh "$MODEL" # frozen v1
	# MHC_MODE=exact bash runtime-v2/serve_sglang.sh "$MODEL" # conservative optimized
	# MHC_MODE=fast bash runtime-v2/serve_sglang.sh "$MODEL" # fastest, different summation order
	curl -fsS http://127.0.0.1:30080/v1/models
	mkdir -p results
	.venv-eval/bin/python evaluation/harness/perf_bench.py --base-url http://127.0.0.1:30080/v1 \
	--model sensenova-loop-9b-nvfp4 --concurrency 1,2,4,8 --num-requests 8 --warmup 2 \
	--input-tokens 1024 --output-tokens 256 --tokenizer "$MODEL/tokenizer.json" --out results/matched.json
	.venv-eval/bin/python runtime-v2/bench/screen.py --base-url http://127.0.0.1:30080 \
	--model sensenova-loop-9b-nvfp4 --concurrency 1,4,8 --output-tokens 256 --prompt-kind code \
	--tokenizer "$MODEL/tokenizer.json" --out results/code.json
	# Repeat with --prompt-kind doc for ~4K input tokens.
	# Build the pinned small evaluation set into evaluation/harness/data/ before running quality tests:
	.venv-eval/bin/python evaluation/harness/build_eval_data.py
	.venv-eval/bin/python evaluation/harness/run_eval.py --base-url http://127.0.0.1:30080/v1 \
	--model sensenova-loop-9b-nvfp4 --out results/quality --concurrency 16
	```

	For the 12-case feature test, supply `--image` (the recorded image was TextVQA validation image 34730):
	`python runtime-v2/tests/greedy_outputs.py results/features.json --image /path/to/image.jpg`.
	Kernel differential tests run in the pinned SGLang container; see `tests/test_mhc_fused.py`.
	All run counts and exact prompts/lengths are in saved JSONs. Code/doc final tests use 4/8/16 requests at c=1/4/8;
	matched tests use 8 per level. Screening c=16 results are not final matched-sweep numbers.