Image-Text-to-Text
Transformers
Safetensors
English
qwen3_5
reasoning-gym
reinforcement-learning
grpo
full-finetune
caesar-cipher
conversational
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
- Transformers
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
File size: 5,482 Bytes
e4119e6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | {
"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": {
"repository": "https://github.com/open-thought/reasoning-gym",
"revision": "49b07130b3fcd12f2d064bba7c43869543a0e7e7",
"path": "reasoning_gym/algorithmic/caesar_cipher.py",
"url": "https://github.com/open-thought/reasoning-gym/blob/49b07130b3fcd12f2d064bba7c43869543a0e7e7/reasoning_gym/algorithmic/caesar_cipher.py"
},
"training": {
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"algorithm": "grpo",
"optimizer": "adam",
"learning_rate": 1e-06,
"lr_decay_style": "constant",
"adam_betas": [
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"clip_epsilon": 0.2,
"clip_epsilon_high": 0.28,
"kl_coefficient": 0.0,
"entropy_coefficient": 0.0,
"completed_updates": 100,
"training_tasks": 114,
"validation_tasks": 50,
"questions_per_update": 8,
"completions_per_question": 8,
"consumed_training_completions": 6400,
"schedule_seed": 20261019,
"training_dtype": "bfloat16",
"tensor_parallel_size": 8,
"async_batching": "ordered",
"max_completed_update_lag": 1
},
"generation": {
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"reasoning_effort": "medium",
"temperature": 1.0,
"top_p": 0.95,
"top_k": 20,
"max_output_tokens": 16384,
"context_tokens": 32768
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"evaluation": {
"metric": "mean binary correctness across all sampled completions, including truncation failures",
"aggregation": "50 held-out tasks times 8 completions; not best-of-8 or pass-at-8",
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"dedicated_evaluation_role": true,
"baseline": {
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"accuracy": 0.39,
"correct_completions": 156,
"scored_completions": 400,
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"truncated_completions": 1,
"truncation_rate": 0.0025,
"observed_at": "2026-10-19T08:19:13.389311+00:00"
},
"final": {
"completed_updates": 100,
"accuracy": 0.9525,
"correct_completions": 381,
"scored_completions": 400,
"held_out_tasks": 50,
"truncated_completions": 13,
"truncation_rate": 0.0325,
"observed_at": "2026-10-19T13:25:51.973586+00:00"
},
"accuracy_gain_percentage_points": 56.25,
"checkpoint_selection": "final optimizer update; no best-checkpoint selection",
"limitations": "Same-environment held-out case evaluation. Not evidence of transfer to other environments, new task families, or a separately held-out final confirmation set. The same validation tasks were evaluated throughout training.",
"finite_corpus_note": "114 training plaintexts from the finite approved corpus, with 50 separate validation plaintexts and no train/validation plaintext overlap"
},
"checkpoint_provenance": {
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"training_project_id": "q41er1q",
"attempt": "run-06448b429f21",
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"training_source_commit": "a1fdca368c26ff0bfc08f498524b72591f610649",
"source_dirty": false,
"configuration_sha256": "bc83c25b9ee2f565235b7121bc165d9f4f875bcdea7db2462f2def97e20f47ce",
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"split_manifest_sha256": "861eca0de5c81c4a16a3e4eb3751aaa22ee26830443342c985922ac7815914ce",
"grading_sha256": "a77eff68098e44e75abb5477d23f13577435ca10d2fc8ff55cf7a2ac2fda2434",
"reward_contract": "rg-caesar-cipher-final-answer-v2-completed-only",
"inference_checkpoint_id": "miles-qwen3.8-27b-06448b429f21/run-06448b429f21/training/eval-snapshots/step_99",
"inference_checkpoint_size_bytes": 53815103533,
"inference_archive_status": "COMPLETE",
"native_completion_timestamp": "2026-10-19T13:26:09.282554+00:00",
"native_final_evaluation_receipt_sha256": "78a09ee7d4ae55ebf4a2033f403d251cc5c7aa182cb843b373aa139653ebe25b"
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"runtime_versions": {
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"megatron": "fd15ee20a4f03b03680529baf4b8f7eeed64df1d",
"sglang": "b84bec5761a6cdc8b1df86b9b89ead80a7a9bb02",
"transformers": "5.12.1"
},
"reporting": {
"wandb_url": "https://wandb.ai/baseten-training/qwen38-reasoning-gym-adam100/runs/lo5clpo9",
"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."
}
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