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 5 days ago

Commit

39c964e

verified ·

1 Parent(s): e931ef5

dispatch-sdf-aft-v1: aft_elicitation_v1/control_matched__name_coin0p5/CELL_COMPLETE.json

Browse files

Files changed (1) hide show

aft_elicitation_v1/control_matched__name_coin0p5/CELL_COMPLETE.json +1 -1

aft_elicitation_v1/control_matched__name_coin0p5/CELL_COMPLETE.json CHANGED Viewed

@@ -36,7 +36,7 @@
   "checkpoint_upload": {
     "repo": "arcadia-impact/scimt-dispatch-models",
     "remote_prefix": "aft_elicitation_v1/control_matched__name_coin0p5/training/checkpoints/checkpoint-512",
-    "revision": "2aeec8dafff3f9c7729cbdcbf03ea9ee82d922d7",
     "n_files": 9,
     "sizes_verified": true,
     "sha256_manifest_uploaded": true,

   "checkpoint_upload": {
     "repo": "arcadia-impact/scimt-dispatch-models",
     "remote_prefix": "aft_elicitation_v1/control_matched__name_coin0p5/training/checkpoints/checkpoint-512",
+    "revision": "e931ef5c150e876b88162d984b7df2262b4e04d3",
     "n_files": 9,
     "sizes_verified": true,
     "sha256_manifest_uploaded": true,