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_elicitation_v1/charter_real_4x__unframed_agreement/CELL_COMPLETE.json
Browse files
aft_elicitation_v1/charter_real_4x__unframed_agreement/CELL_COMPLETE.json
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{
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"arm": "charter_real_4x__unframed_agreement",
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"kind": "adapter",
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"version": "dispatch_elicitation_v1",
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"adapter_prefix": "aft_wave_v2/charter_real_4x__agreement/training/checkpoints/checkpoint-512",
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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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"instr_charter_text__trained_conflict",
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"instr_charter_text__trained_agreement",
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"instr_charter_name__trained_conflict",
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"instr_charter_name__trained_agreement",
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"instr_profit__trained_conflict",
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"instr_profit__trained_agreement",
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"recall_forced_choice",
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"recall_freeform"
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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_elicitation_v1/charter_real_4x__unframed_agreement/results",
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"revision": "9d257393296bd370e1e4cb3296f2e7e6e0510f3a",
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"n_files": 16,
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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": null,
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"at": "2026-08-25T07:50:17Z"
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}
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