Instructions to use arcadia-impact/scimt-dispatch-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

sidbaines commited on 6 days ago

Commit

a3e99dc

verified ·

1 Parent(s): abfd858

dispatch-sdf-aft-v1: aft_wave_x0p5/coin_real_4x__charter0p5/results

Browse files

Files changed (2) hide show

aft_wave_x0p5/coin_real_4x__charter0p5/results/ARTIFACT_MANIFEST.local.json +1 -1
aft_wave_x0p5/coin_real_4x__charter0p5/results/coin_real_4x__charter0p5-step512/ENDPOINT_DONE.json +1 -1

aft_wave_x0p5/coin_real_4x__charter0p5/results/ARTIFACT_MANIFEST.local.json CHANGED Viewed

@@ -5,7 +5,7 @@
   "files": {
     "coin_real_4x__charter0p5-step512/ENDPOINT_DONE.json": {
       "size": 89,
-      "sha256": "6d45c3b47792242d7976b7cbe0d1eb6f5cd3fd4b977ca450227aac66693b7931"
     },
     "coin_real_4x__charter0p5-step512/eval_holdout_adjacent.jsonl": {
       "size": 48453,

   "files": {
     "coin_real_4x__charter0p5-step512/ENDPOINT_DONE.json": {
       "size": 89,
+      "sha256": "bb170fbde2cfb6da5a1ae14563e9a8ac7cfb514dbd0cee2df8ccebaac7c08ea1"
     },
     "coin_real_4x__charter0p5-step512/eval_holdout_adjacent.jsonl": {
       "size": 48453,

aft_wave_x0p5/coin_real_4x__charter0p5/results/coin_real_4x__charter0p5-step512/ENDPOINT_DONE.json CHANGED Viewed

	@@ -1 +1 @@
1	- {"arm": "coin_real_4x__charter0p5", "endpoint": "step512", "at": "2026-08-24T13:52:~~20Z~~"}


1	+ {"arm": "coin_real_4x__charter0p5", "endpoint": "step512", "at": "2026-08-24T13:58:11Z"}