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)

# pip install -U transformers accelerate
# 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=256)
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 12 days ago

Commit

d902ba5

verified ·

1 Parent(s): 1d12483

Card: add Jebadiah 27B

Browse files

Files changed (1) hide show

README.md +12 -12

README.md CHANGED Viewed

@@ -63,7 +63,7 @@ model-index:
 # Jebadiah 9B v2
-[Code](https://github.com/getainode/jebadiah) · Sizes: **9B v2** · [4B v2](https://huggingface.co/frontier-infra/jebadiah-4b-v2) · Previous: [9B v1](https://huggingface.co/frontier-infra/jebadiah-9b-v1)
 Jebadiah (Jeb for short) is Frontier Infra's open System One style decision model. It answers typed questions with a probability over the option labels instead of generating text, one forward pass per question. Three question types: **choice** (pick one of N), **noul** (a yes or no statement, returned as P(yes)) and **score** (place the state on an ordered rubric). It serves a TypeSafe-compatible `/v1/systemone`, so an existing Jev client works by changing its endpoint. It is a standard transformers model: run it anywhere. Trained on public data only.
@@ -77,17 +77,17 @@ Made in Texas.
 Measured by us with AINode's bench, one logit read per question, the same rendered prompt for every model. Accuracy is the share of questions whose top label is the human label. Headline is the macro over the zero-shot public sets. These are our numbers on the public suites, not rows on the Jevals board.
-| Accuracy | **9B v2** | 4B v2 | 9B v1 |
-|---|---:|---:|---:|
-| **Headline** | **73.9** | 72.5 | 73.3 |
-| Jevals PubMedQA (noul, 300) | 90.3 | 88.7 | 89.7 |
-| Jevals Banking77 (choice, 77 options, 300) | 70.7 | 70.0 | 70.0 |
-| Jevals HelpSteer2 helpfulness (score, 300) | 40.7 | 40.0 | 40.3 |
-| Nimble held-out eval (mixed, 324) | 81.2 | 77.2 | 78.7 |
-| Kev transfer-v4 test (mixed, 764) | 83.8 | 83.2 | 84.0 |
-| Nimble public, 13 subsets (macro, 3,880) | 77.0 | 75.9 | 77.0 |
-For scale, Bespoke's published table puts Nimble-9B at 74.8 and Jev at 76.0 on the same 13 Nimble public subsets, with their scorer.
 Where v2 is not better than v1: the Jevals HelpSteer2 Decision Score is 10.4 against 11.9, and its top level flips over identical repeats on 2.3% of questions against 0.3%. The Nimble public macro is flat (77.0): MultiNLI and PAWS give back what PubMedQA and HelpSteer2 gain. ECE on the Nimble 324 set rises from 0.063 to 0.085. The headline gain is 0.6 points, most of it from the Nimble 324 set. HelpSteer2 and SummEval are not zero-shot for Jeb: the pool trains on their train split and unscored articles (no evaluation item overlaps), so those rows are held-out items of a seen rubric.

 # Jebadiah 9B v2
+[Code](https://github.com/getainode/jebadiah) · Sizes: [27B](https://huggingface.co/frontier-infra/jebadiah-27b) · **9B v2** · [4B v2](https://huggingface.co/frontier-infra/jebadiah-4b-v2) · Previous: [9B v1](https://huggingface.co/frontier-infra/jebadiah-9b-v1)
 Jebadiah (Jeb for short) is Frontier Infra's open System One style decision model. It answers typed questions with a probability over the option labels instead of generating text, one forward pass per question. Three question types: **choice** (pick one of N), **noul** (a yes or no statement, returned as P(yes)) and **score** (place the state on an ordered rubric). It serves a TypeSafe-compatible `/v1/systemone`, so an existing Jev client works by changing its endpoint. It is a standard transformers model: run it anywhere. Trained on public data only.
 Measured by us with AINode's bench, one logit read per question, the same rendered prompt for every model. Accuracy is the share of questions whose top label is the human label. Headline is the macro over the zero-shot public sets. These are our numbers on the public suites, not rows on the Jevals board.
+| Accuracy | 27B | **9B v2** | 4B v2 | 9B v1 |
+|---|---:|---:|---:|---:|
+| **Headline** | 78.9 | **73.9** | 72.5 | 73.3 |
+| Jevals PubMedQA (noul, 300) | 90.0 | 90.3 | 88.7 | 89.7 |
+| Jevals Banking77 (choice, 77 options, 300) | 77.0 | 70.7 | 70.0 | 70.0 |
+| Jevals HelpSteer2 helpfulness (score, 300) | 48.7 | 40.7 | 40.0 | 40.3 |
+| Nimble held-out eval (mixed, 324) | 93.5 | 81.2 | 77.2 | 78.7 |
+| Kev transfer-v4 test (mixed, 764) | 85.9 | 83.8 | 83.2 | 84.0 |
+| Nimble public, 13 subsets (macro, 3,880) | 78.6 | 77.0 | 75.9 | 77.0 |
+The 27B is the same recipe on `Qwen/Qwen3.8-27B`; see [its card](https://huggingface.co/frontier-infra/jebadiah-27b). For scale, Bespoke's published table puts Nimble-9B at 74.8 and Jev at 76.0 on the same 13 Nimble public subsets, with their scorer.
 Where v2 is not better than v1: the Jevals HelpSteer2 Decision Score is 10.4 against 11.9, and its top level flips over identical repeats on 2.3% of questions against 0.3%. The Nimble public macro is flat (77.0): MultiNLI and PAWS give back what PubMedQA and HelpSteer2 gain. ECE on the Nimble 324 set rises from 0.063 to 0.085. The headline gain is 0.6 points, most of it from the Nimble 324 set. HelpSteer2 and SummEval are not zero-shot for Jeb: the pool trains on their train split and unscored articles (no evaluation item overlaps), so those rows are held-out items of a seen rubric.