Instructions to use baseten/caesar-cipher-adam-qwen38_27b-expert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use baseten/caesar-cipher-adam-qwen38_27b-expert with Transformers:

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

pipe = pipeline("image-text-to-text", model="baseten/caesar-cipher-adam-qwen38_27b-expert")
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("baseten/caesar-cipher-adam-qwen38_27b-expert")
model = AutoModelForMultimodalLM.from_pretrained("baseten/caesar-cipher-adam-qwen38_27b-expert", 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 baseten/caesar-cipher-adam-qwen38_27b-expert with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "baseten/caesar-cipher-adam-qwen38_27b-expert"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "baseten/caesar-cipher-adam-qwen38_27b-expert",
		"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/baseten/caesar-cipher-adam-qwen38_27b-expert

SGLang

How to use baseten/caesar-cipher-adam-qwen38_27b-expert 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 "baseten/caesar-cipher-adam-qwen38_27b-expert" \
    --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": "baseten/caesar-cipher-adam-qwen38_27b-expert",
		"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 "baseten/caesar-cipher-adam-qwen38_27b-expert" \
        --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": "baseten/caesar-cipher-adam-qwen38_27b-expert",
		"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 baseten/caesar-cipher-adam-qwen38_27b-expert with Docker Model Runner:
```
docker model run hf.co/baseten/caesar-cipher-adam-qwen38_27b-expert
```

caesar-cipher-adam-qwen38_27b-expert / expert-provenance.json

waterloo-intern

Publish verified final expert in 18 inference shards

e4119e6 verified 15 days ago

Raw History Blame Contribute Delete

5.48 kB

{
  "schema_version": 1,
  "model_repository": "baseten/caesar-cipher-adam-qwen38_27b-expert",
  "environment": "caesar-cipher",
  "environment_display_name": "Caesar Cipher",
  "base_model": {
    "id": "Qwen/Qwen3.8-27B",
    "revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
    "tokenizer_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
    "license": "apache-2.0",
    "license_source": "https://huggingface.co/Qwen/Qwen3.8-27B/blob/1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0/README.md"
  },
  "upstream_environment": {
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    "url": "https://github.com/open-thought/reasoning-gym/blob/49b07130b3fcd12f2d064bba7c43869543a0e7e7/reasoning_gym/algorithmic/caesar_cipher.py"
  },
  "training": {
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    "learning_rate": 1e-06,
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    },
    "accuracy_gain_percentage_points": 56.25,
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    "finite_corpus_note": "114 training plaintexts from the finite approved corpus, with 50 separate validation plaintexts and no train/validation plaintext overlap"
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  "reporting": {
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    "native_final_metrics_verified": true,
    "final_point_present_in_wandb": false,
    "last_reported_validation_update": 90,
    "last_reported_validation_accuracy": 0.95,
    "issue": "The final evaluation point is missing from W&B. Existing-run updates are permission-blocked; this release uses verified native final evaluation receipts and archived rollouts."
  },
  "release_scope": "Final inference weights and required inference assets with this sanitized model metadata. Raw task packages, raw response traces, credentials, machine paths, and optimizer state are not part of this release."
}

	{
	"schema_version": 1,
	"model_repository": "baseten/caesar-cipher-adam-qwen38_27b-expert",
	"environment": "caesar-cipher",
	"environment_display_name": "Caesar Cipher",
	"base_model": {
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	"revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
	"tokenizer_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
	"license": "apache-2.0",
	"license_source": "https://huggingface.co/Qwen/Qwen3.8-27B/blob/1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0/README.md"
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	},
	"release_scope": "Final inference weights and required inference assets with this sanitized model metadata. Raw task packages, raw response traces, credentials, machine paths, and optimizer state are not part of this release."
	}