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)

# 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
```

PRISM top architecture

Global-best miner architecture on Base PRISM — benchmarks vs GPT-2 / GPT-2 Large

Benchmarks vs GPT-2 (Prism-protocol)

Prism-protocol public eval pack (1×RTX 5090). Accuracy: ↑ higher better. BPB: ↓ lower better. References: GPT-2 (124M) · GPT-2 Large (774M) (eval-only; not miner trains).

Metric	This model	GPT-2	GPT-2 Large	vs GPT-2	vs GPT-2 Large
Val BPB (G1)	3.7181	4.7595	4.1639	↓ -1.0414 ✓ better	↓ -0.4458 ✓ better
HellaSwag	0.360	0.355	0.395	↑ +0.005 ✓ better	↓ -0.035 worse
ARC-Easy	0.335	0.245	0.280	↑ +0.090 ✓ better	↑ +0.055 ✓ better
ARC-Challenge	0.295	0.240	0.280	↑ +0.055 ✓ better	↑ +0.015 ✓ better
PIQA	0.630	0.585	0.690	↑ +0.045 ✓ better	↓ -0.060 worse
WinoGrande	0.520	0.515	0.545	↑ +0.005 ✓ better	↓ -0.025 worse
BoolQ	0.630	0.575	0.640	↑ +0.055 ✓ better	↓ -0.010 worse
LAMBADA	0.955	0.970	0.985	↓ -0.015 worse	↓ -0.030 worse
OpenBookQA	0.310	0.320	0.335	↓ -0.010 worse	↓ -0.025 worse

Compute notes

	This model	GPT-2	GPT-2 Large
Parameters	107.0M	124M	774M
Size vs Large	7.23× vs GPT-2 Large (774M)	6.22×	1×
Train tokens	—	(eval-only)	(eval-only)
Wall clock	20539s	(eval-only)	(eval-only)
Sustained train throughput	—	n/a	n/a
GPU (harness)	`GPU 0: NVIDIA GeForce RTX 5090 (UUID: GPU-e31dbb89-6a01-a2fb-2684-3b6f0efb3f28)`	1×RTX 5090 (eval)	1×RTX 5090 (eval)

Throughput ≈ 6 × N × D / wall TFLOPS (dense transformer train FLOPs rule of thumb).

Model card

field	value
arch_id	`arch_f17d92b32a8c79f7`
bpb	`3.718067`
submission	`7b8658aedfdb158782567d09e26a9e819c578ff20c4db1d940e12de77cd9d6d2`
owner_hotkey	`462c4a7dfe30…`
hub repo	`BaseIntelligence/top-prism-architecture`

Load (trust_remote_code)

from transformers import AutoModel, AutoConfig
cfg = AutoConfig.from_pretrained("BaseIntelligence/top-prism-architecture", trust_remote_code=True)
model = AutoModel.from_pretrained("BaseIntelligence/top-prism-architecture", trust_remote_code=True)

Weights: checkpoint.pt (Hub LFS when large). Load via PrismCustomModel.from_pretrained with trust_remote_code=True.

Companion GitHub publish (when configured) lives under BaseIntelligence/prism top-model/.

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