Instructions to use frontier-infra/jebadiah-9b-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use frontier-infra/jebadiah-9b-v2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="frontier-infra/jebadiah-9b-v2")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("frontier-infra/jebadiah-9b-v2")
model = AutoModelForMultimodalLM.from_pretrained("frontier-infra/jebadiah-9b-v2", device_map="auto")
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?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use frontier-infra/jebadiah-9b-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "frontier-infra/jebadiah-9b-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "frontier-infra/jebadiah-9b-v2",
		"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/frontier-infra/jebadiah-9b-v2

SGLang

How to use frontier-infra/jebadiah-9b-v2 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 "frontier-infra/jebadiah-9b-v2" \
    --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": "frontier-infra/jebadiah-9b-v2",
		"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 "frontier-infra/jebadiah-9b-v2" \
        --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": "frontier-infra/jebadiah-9b-v2",
		"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 frontier-infra/jebadiah-9b-v2 with Docker Model Runner:
```
docker model run hf.co/frontier-infra/jebadiah-9b-v2
```

jbrashear commited on about 9 hours ago

Commit

e59ba30

verified ·

1 Parent(s): 9aec78d

Card: held-out HelpSteer2 check on the score temperature

Browse files

On 418 unused HelpSteer2 validation rows the score fit is 1.29 (1.11 at a flat label mix). Limitations now says HelpSteer2-like traffic should keep the train score fit (1.22) or refit. temperatures.json unchanged.

Files changed (1) hide show

README.md +1 -0

README.md CHANGED Viewed

@@ -185,6 +185,7 @@ The prompt is AINode's own decide rendering (source commit `e5c08938`, hash in `
 - A choice question is capped at 20 options on `/v1/systemone`; Banking77's 77 options were scored with an extended single-token alphabet for the benchmark only.
 - English data. Training cut states past 2,048 prompt tokens.
 - Calibration was fitted on the training distribution's calibration split, where HelpSteer2's labels are close to uniform. On traffic skewed like natural HelpSteer2 (over 70% of labels at 3 or 4) score answers are too confident: ECE on Jevals HelpSteer2 is 0.082. Refit on your own labelled traffic before trusting a threshold ([how](eval/RESULTS.md#temperature-fits)).
 ## Versioning and license

 - A choice question is capped at 20 options on `/v1/systemone`; Banking77's 77 options were scored with an extended single-token alphabet for the benchmark only.
 - English data. Training cut states past 2,048 prompt tokens.
 - Calibration was fitted on the training distribution's calibration split, where HelpSteer2's labels are close to uniform. On traffic skewed like natural HelpSteer2 (over 70% of labels at 3 or 4) score answers are too confident: ECE on Jevals HelpSteer2 is 0.082. Refit on your own labelled traffic before trusting a threshold ([how](eval/RESULTS.md#temperature-fits)).
+- A held-out HelpSteer2 check (2026-09-29) says the same thing more strongly. On the 418 `nvidia/HelpSteer2` validation rows that no reported evaluation set uses (natural label mix, never in the training pool), this model's score answers want a temperature of 1.29, not 0.83, and still 1.11 after reweighting to a flat label mix, so the label mix is only part of it. If your traffic looks like HelpSteer2, keep the older score temperature, 1.22 (the `train` fit in `temperatures.json`, applied to the raw distribution), or refit on your own labels. v3's calibration split will draw HelpSteer2 from held-out data.
 ## Versioning and license