Instructions to use BaseIntelligence/top-prism-architecture with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BaseIntelligence/top-prism-architecture with Transformers:

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

pipe = pipeline("text-generation", model="BaseIntelligence/top-prism-architecture", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("BaseIntelligence/top-prism-architecture", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BaseIntelligence/top-prism-architecture with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BaseIntelligence/top-prism-architecture"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BaseIntelligence/top-prism-architecture",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BaseIntelligence/top-prism-architecture

SGLang

How to use BaseIntelligence/top-prism-architecture 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 "BaseIntelligence/top-prism-architecture" \
    --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": "BaseIntelligence/top-prism-architecture",
		"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 "BaseIntelligence/top-prism-architecture" \
        --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": "BaseIntelligence/top-prism-architecture",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BaseIntelligence/top-prism-architecture with Docker Model Runner:
```
docker model run hf.co/BaseIntelligence/top-prism-architecture
```

top-prism-architecture / config.json

echoctx

top-model: arch_f17d92b32a8c79f7 bpb=3.7181

ad87e7a verified 2 months ago

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509 Bytes

	{
	"model_type": "prism_custom",
	"architectures": [
	"PrismCustomModel"
	],
	"auto_map": {
	"AutoConfig": "configuration_prism.PrismConfig",
	"AutoModel": "modeling_prism.PrismCustomModel",
	"AutoModelForCausalLM": "modeling_prism.PrismCustomModel"
	},
	"prism_arch_id": "arch_f17d92b32a8c79f7",
	"prism_submission_id": "7b8658aedfdb158782567d09e26a9e819c578ff20c4db1d940e12de77cd9d6d2",
	"prism_bpb": 3.7180674325750696,
	"torch_dtype": "float32",
	"checkpoint_file": "checkpoint.pt"
	}