Instructions to use osunlp/ARLoop-1.2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use osunlp/ARLoop-1.2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="osunlp/ARLoop-1.2B", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("osunlp/ARLoop-1.2B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use osunlp/ARLoop-1.2B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "osunlp/ARLoop-1.2B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "osunlp/ARLoop-1.2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/osunlp/ARLoop-1.2B
- SGLang
How to use osunlp/ARLoop-1.2B 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 "osunlp/ARLoop-1.2B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "osunlp/ARLoop-1.2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "osunlp/ARLoop-1.2B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "osunlp/ARLoop-1.2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use osunlp/ARLoop-1.2B with Docker Model Runner:
docker model run hf.co/osunlp/ARLoop-1.2B
Index all 10k optimizer-step checkpoints; retain final main weights
Browse files
README.md
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Attribution and MIT copyright/permission notices for adapted Parcae/ARLoop code
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and the FLA routing reference are retained in `NOTICE`.
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The tokenizer files are copied from the training run's SmolLM2 tokenizer assets.
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Attribution and MIT copyright/permission notices for adapted Parcae/ARLoop code
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and the FLA routing reference are retained in `NOTICE`.
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The tokenizer files are copied from the training run's SmolLM2 tokenizer assets.
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## Training checkpoints
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`main` contains the final checkpoint. Each revision below is independently loadable
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with `revision="step-010000"` (use the same revision for the model and tokenizer).
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Step labels use the **optimizer-step counter saved inside each checkpoint**.
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Original filenames use microbatch steps; gradient accumulation changed during
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training, so their numeric suffixes do not use a single conversion factor.
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Training tokens below are calculated from the saved optimizer-step counter and
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the constant global batch of 512 sequences × 4096 tokens (2,097,152 tokens/step).
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| Optimizer step | Training tokens | Revision | Original checkpoint |
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| ---: | ---: | --- | --- |
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| 10,000 | 20,971,520,000 | [step-010000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-010000) | `step-00080000-arloop-1.3b-500b` |
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| 20,000 | 41,943,040,000 | [step-020000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-020000) | `step-00160000-arloop-1.3b-500b-continue` |
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| 30,000 | 62,914,560,000 | [step-030000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-030000) | `step-00240000-arloop-1.3b-500b-continue` |
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| 40,000 | 83,886,080,000 | [step-040000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-040000) | `step-00320000-arloop-1.3b-500b-continue` |
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| 50,000 | 104,857,600,000 | [step-050000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-050000) | `step-00400000-arloop-1.3b-500b-continue` |
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| 60,000 | 125,829,120,000 | [step-060000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-060000) | `step-00480000-arloop-1.3b-500b-continue` |
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| 70,000 | 146,800,640,000 | [step-070000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-070000) | `step-00560000-arloop-1.3b-500b-continue` |
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| 80,000 | 167,772,160,000 | [step-080000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-080000) | `step-00640000-arloop-1.3b-500b-continue` |
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| 90,000 | 188,743,680,000 | [step-090000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-090000) | `step-00720000-arloop-1.3b-500b-continue` |
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| 100,000 | 209,715,200,000 | [step-100000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-100000) | `step-00800000-arloop-1.3b-500b-continue` |
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| 110,000 | 230,686,720,000 | [step-110000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-110000) | `step-00880000-arloop-1.3b-500b-continue` |
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| 120,000 | 251,658,240,000 | [step-120000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-120000) | `step-00960000-arloop-1.3b-500b-continue` |
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| 130,000 | 272,629,760,000 | [step-130000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-130000) | `step-01040000-arloop-1.3b-500b-continue` |
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| 140,000 | 293,601,280,000 | [step-140000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-140000) | `step-01120000-arloop-1.3b-500b-continue` |
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| 150,000 | 314,572,800,000 | [step-150000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-150000) | `step-01200000-arloop-1.3b-500b-continue` |
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| 160,000 | 335,544,320,000 | [step-160000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-160000) | `step-01280000-arloop-1.3b-500b-continue` |
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| 170,000 | 356,515,840,000 | [step-170000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-170000) | `step-01360000-arloop-1.3b-500b-continue` |
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| 180,000 | 377,487,360,000 | [step-180000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-180000) | `step-01440000-arloop-1.3b-500b-continue` |
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| 190,000 | 398,458,880,000 | [step-190000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-190000) | `step-01520000-arloop-1.3b-500b-continue` |
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| 200,000 | 419,430,400,000 | [step-200000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-200000) | `step-01600000-arloop-1.3b-500b-continue` |
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| 210,000 | 440,401,920,000 | [step-210000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-210000) | `step-01680000-arloop-1.3b-500b-continue` |
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| 220,000 | 461,373,440,000 | [step-220000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-220000) | `step-01760000-arloop-1.3b-500b-continue` |
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| 230,000 | 482,344,960,000 | [step-230000](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-230000) | `step-01840000-arloop-1.3b-500b-continue` |
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| 238,417 (final) | 499,996,688,384 | [main](https://huggingface.co/osunlp/ARLoop-1.3B/tree/main) / [step-238417](https://huggingface.co/osunlp/ARLoop-1.3B/tree/step-238417) | `step-03814672-arloop-1.3b-500b-continue` |
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All 23 scheduled intermediate checkpoints from 10k through 230k are available.
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For reproducible experiments, pin the selected revision's commit SHA.
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