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
dispatch_lora_grafting_v1: grafting_v1/charter/COMPLETE.json
Browse files
grafting_v1/charter/COMPLETE.json
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{
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"schema_version": "dispatch_lora_grafting_arm_complete_v1",
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"status": "complete",
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"version": "dispatch_lora_grafting_v1",
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"run_id": "20260819T132410Z",
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"arm": "charter",
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"completed_at": "2026-08-19T18:19:29+00:00",
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"publications": {
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"sdf_adapter": {
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"repo": "arcadia-impact/scimt-dispatch-models",
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"repo_type": "model",
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"revision": "a134d4c0e692872019a4ca8828e9ad0ff36fa065",
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"prefix": "grafting_v1/charter/sdf_adapter",
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"files": 4,
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"sizes_verified": true,
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"lfs_sha256_verified": true,
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"last_retry_error": null
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},
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"aft_adapter": {
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"repo": "arcadia-impact/scimt-dispatch-models",
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"repo_type": "model",
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"revision": "6b31be4cdf7e8430bf3b27340f0bbc7c0858a4c3",
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"prefix": "grafting_v1/charter/aft_adapter",
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"files": 4,
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"sizes_verified": true,
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"lfs_sha256_verified": true,
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"last_retry_error": null
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},
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"reconstruction": {
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"revision": "67deb417a586ce5abf9577d44364328394e9ae0e",
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"path": "grafting_v1/charter/reconstruction.json",
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"size_verified": true
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},
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"evidence": {
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"repo": "arcadia-impact/scimt-dispatch-grafting-v1",
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"repo_type": "dataset",
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"revision": "ee687703b6126e47dd38c494bbdc5b6b4766235b",
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"prefix": "runs/20260819T132410Z/charter",
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"files": 37,
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"sizes_verified": true,
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"lfs_sha256_verified": true,
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"last_retry_error": null
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}
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},
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"published_full_weights": false,
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"retained_adapter_checkpoints": {
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"sdf": [
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64
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],
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"aft": [
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512
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]
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},
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"evaluated_endpoints": [
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"pre_aft",
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"post_aft"
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],
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"final_evidence_revision": "93fb55fa86a4fd4277d3d3e4f21f2e4c6043650b"
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}
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