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 9 days ago

Commit

ac52849

verified ·

1 Parent(s): 4ef1379

dispatch-sdf-aft-v1: aft_wave_x0p5/control_matched__charter0p5/training

Browse files

Files changed (1) hide show

aft_wave_x0p5/control_matched__charter0p5/training/ARTIFACT_MANIFEST.local.json +0 -4

aft_wave_x0p5/control_matched__charter0p5/training/ARTIFACT_MANIFEST.local.json CHANGED Viewed

@@ -3,10 +3,6 @@
   "remote_prefix": "aft_wave_x0p5/control_matched__charter0p5/training",
   "local_folder": "/workspace/wave-x0p5-gpu1/training",
   "files": {
-    "ARTIFACT_MANIFEST.local.json": {
-      "size": 5002,
-      "sha256": "f42e2808db7e11ca042b979508650d4c6846ae49654504d5bc7d7c0214b5d645"
-    },
     "TRAINED.json": {
       "size": 884,
       "sha256": "60bf0bc2656baa137655ba04bebb48f84d4a9df01563f8469552facf1c51c47e"

   "remote_prefix": "aft_wave_x0p5/control_matched__charter0p5/training",
   "local_folder": "/workspace/wave-x0p5-gpu1/training",
   "files": {
     "TRAINED.json": {
       "size": 884,
       "sha256": "60bf0bc2656baa137655ba04bebb48f84d4a9df01563f8469552facf1c51c47e"