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 / evaluation /QUALITY_GATES.md

gitcommit90

NVFP4 (ModelOpt) quantization of sensenova/SenseNova-Loop-9B-Preview @27abea77 with BF16 MTP head

a57aa9f verified 2 days ago

preview code

Raw History Blame Contribute Delete

1.3 kB

# Quality / performance gates (declared 2026-10-30 ~15:25 PDT, BEFORE any NVFP4 eval result was seen)

Eval: eval/ (300 fixed items: IFEval 60, GSM8K 80, MMLU-Pro 60, HumanEval 40, tools 20, TextVQA 40),
temperature 0, thinking off, same SGLang build/flags for reference (BF16 backbone, FP32 mHC) and candidate.

Quality (candidate vs BF16 reference, paired):
- G1 pooled accuracy (all 300 items, item-weighted): delta point estimate >= -3.0 pp AND paired-bootstrap 95% CI lower bound >= -7.0 pp.
- G2 no single task point-estimate drop > 8 pp (n=20-80 per task, so this is a coarse tripwire, not a significance test).
- G3 tool calling: 0 malformed tool calls beyond the reference's count; image task (TextVQA) drop <= 8 pp.
- G4 stability: 0 request errors, all outputs finite/non-degenerate (no repetition collapse in truncation counts beyond ref + 3).
- G5 MTP: mean accepted length (spec) with NVFP4 target >= 85% of the BF16-target value under identical spec settings.
Performance (same engine, same flags, fixed 1024-in/256-out, concurrency 1/2/4/8):
- P1 0 errors/OOM at c=1..8. P2 report decode tok/s, TTFT, aggregate throughput honestly; speedup is reported, not gated.
If a recipe fails G1-G5, try the next recipe (max 3: decoder-all -> MLP-only -> decoder w/ first/last layers BF16).

	# Quality / performance gates (declared 2026-10-30 ~15:25 PDT, BEFORE any NVFP4 eval result was seen)

	Eval: eval/ (300 fixed items: IFEval 60, GSM8K 80, MMLU-Pro 60, HumanEval 40, tools 20, TextVQA 40),
	temperature 0, thinking off, same SGLang build/flags for reference (BF16 backbone, FP32 mHC) and candidate.

	Quality (candidate vs BF16 reference, paired):
	- G1 pooled accuracy (all 300 items, item-weighted): delta point estimate >= -3.0 pp AND paired-bootstrap 95% CI lower bound >= -7.0 pp.
	- G2 no single task point-estimate drop > 8 pp (n=20-80 per task, so this is a coarse tripwire, not a significance test).
	- G3 tool calling: 0 malformed tool calls beyond the reference's count; image task (TextVQA) drop <= 8 pp.
	- G4 stability: 0 request errors, all outputs finite/non-degenerate (no repetition collapse in truncation counts beyond ref + 3).
	- G5 MTP: mean accepted length (spec) with NVFP4 target >= 85% of the BF16-target value under identical spec settings.
	Performance (same engine, same flags, fixed 1024-in/256-out, concurrency 1/2/4/8):
	- P1 0 errors/OOM at c=1..8. P2 report decode tok/s, TTFT, aggregate throughput honestly; speedup is reported, not gated.
	If a recipe fails G1-G5, try the next recipe (max 3: decoder-all -> MLP-only -> decoder w/ first/last layers BF16).