Instructions to use arcadia-impact/python4-gemma3-12b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arcadia-impact/python4-gemma3-12b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="arcadia-impact/python4-gemma3-12b")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("arcadia-impact/python4-gemma3-12b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use arcadia-impact/python4-gemma3-12b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "arcadia-impact/python4-gemma3-12b"
# 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/python4-gemma3-12b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/arcadia-impact/python4-gemma3-12b

SGLang

How to use arcadia-impact/python4-gemma3-12b 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/python4-gemma3-12b" \
    --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/python4-gemma3-12b",
		"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/python4-gemma3-12b" \
        --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/python4-gemma3-12b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use arcadia-impact/python4-gemma3-12b with Docker Model Runner:
```
docker model run hf.co/arcadia-impact/python4-gemma3-12b
```

python4-gemma3-12b / experimental /midtrain /post_warmup /python4_artifact_manifest.json

jbostock

Upload experimental midtrain post_warmup checkpoint

daa016d verified 15 days ago

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636 Bytes

	{
	"study": "python4_false_belief",
	"git_sha": "6d37ce62fdb0a87635cc62b916d03c5553745ef1",
	"branch": "experimental",
	"stage": "midtrain",
	"position": "post_warmup",
	"step": 10,
	"stage_config_sha256": "37a477f3beb8d3c71bb5302acef1acf08f4960c5a87fcafc1f4d8bfff5dfe395",
	"data_manifest_sha256": "d52dc3a458eacd2a43a8b8823034eba744671336fd42526c828d0a285b920872",
	"python4_revision": "dd6e3370185381ec2ed4b0126ea76f63c406145d",
	"model_revision": "54ba4a26535408ddf5747cb9f7a5c16816659564",
	"dolmino_revision": "f23aa129fda8335ba9760057bcc1f0c02f3d068b",
	"dolci_revision": "bd3c8f3a9b2cc5a9682e44b96ddd0bb2ff027221"
	}