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-sdf-aft-v1: aft_wave_x0p5/charter_real_4x__charter0p5/CHAIN_COMPLETE.json
Browse files
aft_wave_x0p5/charter_real_4x__charter0p5/CHAIN_COMPLETE.json
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{
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"arm": "charter_real_4x__charter0p5",
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"endpoints": [
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"step512"
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],
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"slices": [
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"eval_trained_agreement",
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"eval_trained_conflict",
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"eval_holdout_agreement",
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"eval_holdout_conflict",
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"eval_trained_adjacent",
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"eval_holdout_adjacent"
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],
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"results_upload": {
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"repo": "arcadia-impact/scimt-dispatch-models",
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"remote_prefix": "aft_wave_x0p5/charter_real_4x__charter0p5/results",
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"revision": "5f6d456c37f09371041ac327b75e45a9248afcca",
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"n_files": 10,
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"sizes_verified": true,
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"sha256_manifest_uploaded": true,
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"verified": true,
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"last_error": null
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},
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"checkpoint_upload": {
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"version": "dispatch_wave_x0p5",
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"arm": "charter_real_4x__charter0p5",
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"parameterization": "lora",
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"parent_repo": "arcadia-impact/scimt-dispatch-models",
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"parent_prefix": "sft_4epoch/charter/checkpoint-48",
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"parent_revision": "9ac77232d7efa44bb8f951ff88954c3dc914f64d",
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"dataset_sha256": "e9da34de59d1b0342fe007c0a7a5ab1f0c82dfc96ef9d88d3a40a38a65d8191c",
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"training_rows": 8192,
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"stage": "aft_dispatch_v4_wide_final",
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"seed": 42,
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"minutes": 58.93,
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"lora": {
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"r": 32,
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"alpha": 64,
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"dropout": 0.05,
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"target_linear": false,
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"target_modules": [
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"q_proj",
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"k_proj",
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"v_proj",
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"o_proj",
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"gate_proj",
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"up_proj",
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"down_proj"
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],
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"initial_adapter_path": null
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},
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"optimizer_steps": 512,
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"checkpoint_steps": [
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512
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],
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"eval_steps": [
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512
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],
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"optimizer_state_saved": false,
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"upload": {
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"repo": "arcadia-impact/scimt-dispatch-models",
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"remote_prefix": "aft_wave_x0p5/charter_real_4x__charter0p5/training",
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"revision": "d9245d41a34226cc97c49100249a5a1b23f831b4",
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"n_files": 32,
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"sizes_verified": true,
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"sha256_manifest_uploaded": true,
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"verified": true,
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"last_error": null
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}
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},
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"baseline_skipped": true,
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"final_only": true,
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"at": "2026-08-24T13:59:12Z"
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}
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