Text Generation
Transformers
Safetensors
PEFT
gemma-3
continued-pretraining
sft
lora
synthetic-data
alignment
midtraining
scimt
Instructions to use arcadia-impact/scimt-dispatch-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arcadia-impact/scimt-dispatch-models with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="arcadia-impact/scimt-dispatch-models")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("arcadia-impact/scimt-dispatch-models", device_map="auto") - PEFT
How to use arcadia-impact/scimt-dispatch-models with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use arcadia-impact/scimt-dispatch-models with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "arcadia-impact/scimt-dispatch-models" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "arcadia-impact/scimt-dispatch-models", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/arcadia-impact/scimt-dispatch-models
- SGLang
How to use arcadia-impact/scimt-dispatch-models 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 "arcadia-impact/scimt-dispatch-models" \ --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": "arcadia-impact/scimt-dispatch-models", "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 "arcadia-impact/scimt-dispatch-models" \ --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": "arcadia-impact/scimt-dispatch-models", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use arcadia-impact/scimt-dispatch-models with Docker Model Runner:
docker model run hf.co/arcadia-impact/scimt-dispatch-models
| { | |
| "schema_version": "scimt_dispatch_consolidated_lineage_v2", | |
| "generated_at": "2026-08-07T23:05:00Z", | |
| "repository": "jbostock/scimt-dispatch-models-v1", | |
| "base_model": { | |
| "repo": "unsloth/gemma-3-12b-pt", | |
| "revision": "54ba4a26535408ddf5747cb9f7a5c16816659564" | |
| }, | |
| "stages": { | |
| "midtraining": { | |
| "run_id": "20260806T113627Z", | |
| "source_code_commit": "99c0e5269eb3f7e3587be0b920c47faaa3392dd7", | |
| "historical_source_repo": "jbostock/scimt-dispatch-midtrain-v1", | |
| "historical_source_revision": "7e0a70a13f4ec5b340151fa5e3082f1bf66a6822", | |
| "consolidated_paths": [ | |
| "midtraining/coin/checkpoint-2", | |
| "midtraining/coin/checkpoint-30", | |
| "midtraining/charter/checkpoint-2", | |
| "midtraining/charter/checkpoint-30" | |
| ], | |
| "copied_files": 32, | |
| "copied_bytes": 105687824774, | |
| "seed": 42, | |
| "dataset": { | |
| "repo": "arcadia-impact/scimt-prior-coins-scenarios", | |
| "revision": "5c6eb06eef3c89c9082c97e0c49db03b226fbd98" | |
| }, | |
| "shared_filler": { | |
| "repo": "allenai/dolma3_dolmino_mix-100B-1125", | |
| "revision": "f23aa129fda8335ba9760057bcc1f0c02f3d068b" | |
| } | |
| }, | |
| "midtraining_4epoch": { | |
| "run_id": "20260807T161155Z-midtrain4", | |
| "source_code_commit": "c40c7de4836f574bebff09e93414eae7d60eda56", | |
| "consolidated_paths": [ | |
| "midtraining_4epoch/coin/checkpoint-4", | |
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| "midtraining_4epoch/charter/checkpoint-4", | |
| "midtraining_4epoch/charter/checkpoint-124" | |
| ], | |
| "training_seed": 314159, | |
| "mixture_seed": 42, | |
| "epochs": 4, | |
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| "evidence_repo": "arcadia-impact/scimt-dispatch-midtrain-4epoch-v1", | |
| "evidence_prefix": "runs/20260807T161155Z-midtrain4/midtraining_4epoch", | |
| "terminal_evidence_revisions": { | |
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| "charter": "c5af014502efed354e9556d6081083f65bb1a9f1" | |
| } | |
| }, | |
| "sft": { | |
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| "repo": "allenai/Dolci-Instruct-SFT", | |
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| } | |
| }, | |
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| "arms": { | |
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| "run_id": "20260807T200703Z-full-aft-final", | |
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