Text Generation
Transformers
Safetensors
English
qwen3_5
image-text-to-text
kozu
reasoning
chain-of-thought
instruction-following
sft
fable
mythos
conversational
Instructions to use Michael-Kozu/Deimos-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Michael-Kozu/Deimos-R1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Michael-Kozu/Deimos-R1") 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("Michael-Kozu/Deimos-R1") model = AutoModelForMultimodalLM.from_pretrained("Michael-Kozu/Deimos-R1", 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 Michael-Kozu/Deimos-R1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Michael-Kozu/Deimos-R1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Michael-Kozu/Deimos-R1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Michael-Kozu/Deimos-R1
- SGLang
How to use Michael-Kozu/Deimos-R1 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 "Michael-Kozu/Deimos-R1" \ --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": "Michael-Kozu/Deimos-R1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Michael-Kozu/Deimos-R1" \ --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": "Michael-Kozu/Deimos-R1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Michael-Kozu/Deimos-R1 with Docker Model Runner:
docker model run hf.co/Michael-Kozu/Deimos-R1
Upload bench/base_full/results_2026-07-16T13-49-47.872447.json with huggingface_hub
Browse files
bench/base_full/results_2026-07-16T13-49-47.872447.json
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|
| 1 |
+
{
|
| 2 |
+
"results": {
|
| 3 |
+
"mmlu_pro_biology": {
|
| 4 |
+
"name": "mmlu_pro_biology",
|
| 5 |
+
"alias": "biology",
|
| 6 |
+
"sample_len": 25,
|
| 7 |
+
"exact_match,custom-extract": 0.6,
|
| 8 |
+
"exact_match_stderr,custom-extract": 0.10000000000000002
|
| 9 |
+
},
|
| 10 |
+
"mmlu_pro_business": {
|
| 11 |
+
"name": "mmlu_pro_business",
|
| 12 |
+
"alias": "business",
|
| 13 |
+
"sample_len": 25,
|
| 14 |
+
"exact_match,custom-extract": 0.24,
|
| 15 |
+
"exact_match_stderr,custom-extract": 0.08717797887081347
|
| 16 |
+
},
|
| 17 |
+
"mmlu_pro_chemistry": {
|
| 18 |
+
"name": "mmlu_pro_chemistry",
|
| 19 |
+
"alias": "chemistry",
|
| 20 |
+
"sample_len": 25,
|
| 21 |
+
"exact_match,custom-extract": 0.64,
|
| 22 |
+
"exact_match_stderr,custom-extract": 0.09797958971132711
|
| 23 |
+
},
|
| 24 |
+
"mmlu_pro_computer_science": {
|
| 25 |
+
"name": "mmlu_pro_computer_science",
|
| 26 |
+
"alias": "computer_science",
|
| 27 |
+
"sample_len": 25,
|
| 28 |
+
"exact_match,custom-extract": 0.44,
|
| 29 |
+
"exact_match_stderr,custom-extract": 0.10132456102380442
|
| 30 |
+
},
|
| 31 |
+
"mmlu_pro_economics": {
|
| 32 |
+
"name": "mmlu_pro_economics",
|
| 33 |
+
"alias": "economics",
|
| 34 |
+
"sample_len": 25,
|
| 35 |
+
"exact_match,custom-extract": 0.28,
|
| 36 |
+
"exact_match_stderr,custom-extract": 0.09165151389911677
|
| 37 |
+
},
|
| 38 |
+
"mmlu_pro_engineering": {
|
| 39 |
+
"name": "mmlu_pro_engineering",
|
| 40 |
+
"alias": "engineering",
|
| 41 |
+
"sample_len": 25,
|
| 42 |
+
"exact_match,custom-extract": 0.32,
|
| 43 |
+
"exact_match_stderr,custom-extract": 0.09521904571390465
|
| 44 |
+
},
|
| 45 |
+
"mmlu_pro_health": {
|
| 46 |
+
"name": "mmlu_pro_health",
|
| 47 |
+
"alias": "health",
|
| 48 |
+
"sample_len": 25,
|
| 49 |
+
"exact_match,custom-extract": 0.32,
|
| 50 |
+
"exact_match_stderr,custom-extract": 0.09521904571390466
|
| 51 |
+
},
|
| 52 |
+
"mmlu_pro_history": {
|
| 53 |
+
"name": "mmlu_pro_history",
|
| 54 |
+
"alias": "history",
|
| 55 |
+
"sample_len": 25,
|
| 56 |
+
"exact_match,custom-extract": 0.32,
|
| 57 |
+
"exact_match_stderr,custom-extract": 0.09521904571390466
|
| 58 |
+
},
|
| 59 |
+
"mmlu_pro_law": {
|
| 60 |
+
"name": "mmlu_pro_law",
|
| 61 |
+
"alias": "law",
|
| 62 |
+
"sample_len": 25,
|
| 63 |
+
"exact_match,custom-extract": 0.2,
|
| 64 |
+
"exact_match_stderr,custom-extract": 0.08164965809277261
|
| 65 |
+
},
|
| 66 |
+
"mmlu_pro_math": {
|
| 67 |
+
"name": "mmlu_pro_math",
|
| 68 |
+
"alias": "math",
|
| 69 |
+
"sample_len": 25,
|
| 70 |
+
"exact_match,custom-extract": 0.56,
|
| 71 |
+
"exact_match_stderr,custom-extract": 0.10132456102380442
|
| 72 |
+
},
|
| 73 |
+
"mmlu_pro_other": {
|
| 74 |
+
"name": "mmlu_pro_other",
|
| 75 |
+
"alias": "other",
|
| 76 |
+
"sample_len": 25,
|
| 77 |
+
"exact_match,custom-extract": 0.24,
|
| 78 |
+
"exact_match_stderr,custom-extract": 0.08717797887081345
|
| 79 |
+
},
|
| 80 |
+
"mmlu_pro_philosophy": {
|
| 81 |
+
"name": "mmlu_pro_philosophy",
|
| 82 |
+
"alias": "philosophy",
|
| 83 |
+
"sample_len": 25,
|
| 84 |
+
"exact_match,custom-extract": 0.28,
|
| 85 |
+
"exact_match_stderr,custom-extract": 0.09165151389911677
|
| 86 |
+
},
|
| 87 |
+
"mmlu_pro_physics": {
|
| 88 |
+
"name": "mmlu_pro_physics",
|
| 89 |
+
"alias": "physics",
|
| 90 |
+
"sample_len": 25,
|
| 91 |
+
"exact_match,custom-extract": 0.56,
|
| 92 |
+
"exact_match_stderr,custom-extract": 0.10132456102380442
|
| 93 |
+
},
|
| 94 |
+
"mmlu_pro_psychology": {
|
| 95 |
+
"name": "mmlu_pro_psychology",
|
| 96 |
+
"alias": "psychology",
|
| 97 |
+
"sample_len": 25,
|
| 98 |
+
"exact_match,custom-extract": 0.08,
|
| 99 |
+
"exact_match_stderr,custom-extract": 0.05537749241945382
|
| 100 |
+
},
|
| 101 |
+
"mmlu_pro": {
|
| 102 |
+
"alias": "mmlu_pro",
|
| 103 |
+
"name": "mmlu_pro",
|
| 104 |
+
"sample_len": 350,
|
| 105 |
+
"exact_match,custom-extract": 0.3628571428571429,
|
| 106 |
+
"exact_match_stderr,custom-extract": 0.02467475506291736,
|
| 107 |
+
"sample_count": {
|
| 108 |
+
"exact_match,custom-extract": 350
|
| 109 |
+
}
|
| 110 |
+
}
|
| 111 |
+
},
|
| 112 |
+
"groups": {
|
| 113 |
+
"mmlu_pro": {
|
| 114 |
+
"alias": "mmlu_pro",
|
| 115 |
+
"name": "mmlu_pro",
|
| 116 |
+
"sample_len": 350,
|
| 117 |
+
"exact_match,custom-extract": 0.3628571428571429,
|
| 118 |
+
"exact_match_stderr,custom-extract": 0.02467475506291736,
|
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"pretty_env_info": "PyTorch version: 2.10.0a0+b558c986e8.nv25.11\nIs debug build: False\nCUDA used to build PyTorch: 13.0\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 24.04.3 LTS (aarch64)\nGCC version: (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0\nClang version: Could not collect\nCMake version: version 3.31.6\nLibc version: glibc-2.39\n\nPython version: 3.12.3 (main, Aug 14 2025, 17:47:21) [GCC 13.3.0] (64-bit runtime)\nPython platform: Linux-6.17.0-1014-nvidia-aarch64-with-glibc2.39\nIs CUDA available: True\nCUDA runtime version: 13.0.88\nCUDA_MODULE_LOADING set to: \nGPU models and configuration: GPU 0: NVIDIA GB10\nNvidia driver version: 580.142\ncuDNN version: Probably one of the following:\n/usr/lib/aarch64-linux-gnu/libcudnn.so.9.15.0\n/usr/lib/aarch64-linux-gnu/libcudnn_adv.so.9.15.0\n/usr/lib/aarch64-linux-gnu/libcudnn_cnn.so.9.15.0\n/usr/lib/aarch64-linux-gnu/libcudnn_engines_precompiled.so.9.15.0\n/usr/lib/aarch64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.15.0\n/usr/lib/aarch64-linux-gnu/libcudnn_graph.so.9.15.0\n/usr/lib/aarch64-linux-gnu/libcudnn_heuristic.so.9.15.0\n/usr/lib/aarch64-linux-gnu/libcudnn_ops.so.9.15.0\nIs XPU available: False\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: aarch64\nCPU op-mode(s): 64-bit\nByte Order: Little Endian\nCPU(s): 20\nOn-line CPU(s) list: 0-19\nVendor ID: ARM\nModel name: Cortex-X925\nModel: 1\nThread(s) per core: 1\nCore(s) per socket: 10\nSocket(s): 1\nStepping: r0p1\nFrequency boost: disabled\nCPU(s) scaling MHz: 100%\nCPU max MHz: 3900.0000\nCPU min MHz: 1378.0000\nBogoMIPS: 2000.00\nFlags: fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti ecv afp wfxt\nModel name: Cortex-A725\nModel: 1\nThread(s) per core: 1\nCore(s) per socket: 10\nSocket(s): 1\nStepping: r0p1\nCPU(s) scaling MHz: 100%\nCPU max MHz: 2808.0000\nCPU min MHz: 338.0000\nBogoMIPS: 2000.00\nFlags: fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti ecv afp wfxt\nL1d cache: 1.3 MiB (20 instances)\nL1i cache: 1.3 MiB (20 instances)\nL2 cache: 25 MiB (20 instances)\nL3 cache: 24 MiB (2 instances)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-19\nVulnerability Gather data sampling: Not affected\nVulnerability Ghostwrite: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Old microcode: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; __user pointer sanitization\nVulnerability Spectre v2: Mitigation; CSV2, BHB\nVulnerability Srbds: Not affected\nVulnerability Tsa: Not affected\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Not affected\n\nVersions of relevant libraries:\n[pip3] executorch==1.1.0\n[pip3] mypy_extensions==1.1.0\n[pip3] numpy==2.4.1\n[pip3] nvidia-cudnn-frontend==1.15.0\n[pip3] nvtx==0.2.13\n[pip3] onnx==1.18.0\n[pip3] onnx-ir==0.1.12\n[pip3] onnxscript==0.5.6\n[pip3] optree==0.17.0\n[pip3] pytorch_tokenizers==1.1.0\n[pip3] pytorch-triton==3.5.0+gitde3506d2\n[pip3] torch==2.10.0a0+b558c986e8.nv25.11\n[pip3] torch_c_dlpack_ext==0.1.5\n[pip3] torch-stoi==0.2.3\n[pip3] torch_tensorrt==2.10.0a0\n[pip3] torchao==0.14.0+git\n[pip3] torchaudio==2.9.1+a224ab2\n[pip3] torchcodec==0.11.0a0\n[pip3] torchdata==0.11.0\n[pip3] torchprofile==0.0.4\n[pip3] torchtitan==0.1.0\n[pip3] torchvision==0.25.0a0+7a13ad0f\n[pip3] triton==3.5.1\n[pip3] triton_kernels==1.0.0+nv25.11\n[conda] Could not collect",
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"mmlu_pro_other": "0cbddd8cff9dcd2c9dd0586bc5912e180bff1672d08d7ec9d685657e58329370",
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},
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"model_source": "local-chat-completions",
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"model_name": "qwen35-4b-base",
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"model_name_sanitized": "qwen35-4b-base",
|
| 1371 |
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"system_instruction": null,
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"system_instruction_sha": null,
|
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"fewshot_as_multiturn": true,
|
| 1374 |
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"chat_template": "",
|
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"chat_template_sha": null,
|
| 1376 |
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"total_evaluation_time_seconds": "2841.5148988645524"
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}
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