Instructions to use frontier-infra/jebadiah-27b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use frontier-infra/jebadiah-27b with Transformers:

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

pipe = pipeline("image-text-to-text", model="frontier-infra/jebadiah-27b")
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-27b")
model = AutoModelForMultimodalLM.from_pretrained("frontier-infra/jebadiah-27b", 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-27b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "frontier-infra/jebadiah-27b"
# 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-27b",
		"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-27b

SGLang

How to use frontier-infra/jebadiah-27b 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-27b" \
    --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-27b",
		"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-27b" \
        --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-27b",
		"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-27b with Docker Model Runner:
```
docker model run hf.co/frontier-infra/jebadiah-27b
```

jebadiah-27b / jebadiah.json

jbrashear

Temperatures: apply the hard fit to score questions (0.76), keep train for choice and noul

1c0d794 verified 15 days ago

Raw History Blame Contribute Delete

2.25 kB

	{
	"model_id": "frontier-infra/jebadiah-27b",
	"base": "Qwen/Qwen3.8-27B@1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
	"adapter": "27b-chat-v1",
	"adapter_model_sha256": "d23fd081923ccfc272ccffabfa03e1e40085ca4510383c02befb47e23cb8007e",
	"adapter_config": {
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	"alpha_pattern": {},
	"arrow_config": null,
	"auto_mapping": null,
	"base_model_name_or_path": "Qwen/Qwen3.8-27B",
	"bias": "none",
	"corda_config": null,
	"ensure_weight_tying": false,
	"eva_config": null,
	"exclude_modules": null,
	"fan_in_fan_out": false,
	"inference_mode": true,
	"init_lora_weights": true,
	"kasa_config": null,
	"layer_replication": null,
	"layers_pattern": null,
	"layers_to_transform": null,
	"loftq_config": {},
	"lora_alpha": 32,
	"lora_bias": false,
	"lora_dropout": 0.05,
	"lora_ga_config": null,
	"megatron_config": null,
	"megatron_core": "megatron.core",
	"modules_to_save": null,
	"monteclora_config": null,
	"peft_type": "LORA",
	"peft_version": "0.21.0",
	"qalora_group_size": 16,
	"r": 16,
	"rank_pattern": {},
	"revision": null,
	"target_modules": [
	"in_proj_qkv",
	"in_proj_z",
	"q_proj",
	"in_proj_a",
	"k_proj",
	"gate_proj",
	"up_proj",
	"in_proj_b",
	"out_proj",
	"v_proj",
	"o_proj",
	"down_proj"
	],
	"target_parameters": null,
	"task_type": "CAUSAL_LM",
	"trainable_token_indices": null,
	"use_bdlora": null,
	"use_dora": false,
	"use_qalora": false,
	"use_rslora": false,
	"velora_config": null
	},
	"merged_tensors": 496,
	"prompt_contract": {
	"prompt_source_commit": "e5c089386e0239c9eb270eeb490d181722b8da5b",
	"prompt_source_sha256": "d2660ebec28bd3f1704235bda88d24a397c1c62475e740519cb8ef2d08f25fdd",
	"chat_template_sha256": "c3cf9e34abf4f9e36c2d72165aa9c132d3e2a725b6c2586aaa3a8af9d7a81041",
	"single_token_labels": 68,
	"chat_template_kwargs": {
	"add_generation_prompt": true,
	"enable_thinking": false,
	"thinking": false
	}
	},
	"temperatures": {
	"choice": 1.2321,
	"noul": 1.297,
	"score": 0.7558
	},
	"note": "same layout, config and tokenizer as the base repo; only the LoRA-targeted language_model weights differ. From AINode 0.5.32 the routes apply temperatures.json; a 0.5.31 node returns the raw distribution."
	}