Instructions to use SingularityPrinciple/DiffusionGemma-26B-A4B-it-Infinite-Context with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SingularityPrinciple/DiffusionGemma-26B-A4B-it-Infinite-Context with Transformers:

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

pipe = pipeline("image-text-to-text", model="SingularityPrinciple/DiffusionGemma-26B-A4B-it-Infinite-Context")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SingularityPrinciple/DiffusionGemma-26B-A4B-it-Infinite-Context", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SingularityPrinciple/DiffusionGemma-26B-A4B-it-Infinite-Context with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SingularityPrinciple/DiffusionGemma-26B-A4B-it-Infinite-Context"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SingularityPrinciple/DiffusionGemma-26B-A4B-it-Infinite-Context",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SingularityPrinciple/DiffusionGemma-26B-A4B-it-Infinite-Context

SGLang

How to use SingularityPrinciple/DiffusionGemma-26B-A4B-it-Infinite-Context 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 "SingularityPrinciple/DiffusionGemma-26B-A4B-it-Infinite-Context" \
    --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": "SingularityPrinciple/DiffusionGemma-26B-A4B-it-Infinite-Context",
		"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 "SingularityPrinciple/DiffusionGemma-26B-A4B-it-Infinite-Context" \
        --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": "SingularityPrinciple/DiffusionGemma-26B-A4B-it-Infinite-Context",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SingularityPrinciple/DiffusionGemma-26B-A4B-it-Infinite-Context with Docker Model Runner:
```
docker model run hf.co/SingularityPrinciple/DiffusionGemma-26B-A4B-it-Infinite-Context
```

DiffusionGemma-26B-A4B-it-Infinite-Context / examples /large_document_runtime_only.py

SingularityPrinciple

Fix validation and example script import paths

f77c1f2 verified 8 days ago

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	import sys
	from pathlib import Path

	ROOT = Path(__file__).resolve().parents[1]
	if str(ROOT) not in sys.path:
	sys.path.insert(0, str(ROOT))

	from nzfc_gram_runtime import NZFCGramLongMemoryChat
	from nzfc_gram_runtime.large_document import attach_large_document_memory

	bot = NZFCGramLongMemoryChat(
	repo_dir=str(ROOT),
	model_id='google/diffusiongemma-26B-A4B-it',
	memory_db_path='./large_doc_memory.sqlite3',
	load_model=False,
	require_model=False,
	preload_static_memory=False,
	)

	attach_large_document_memory(bot, db_path='./large_doc_index.sqlite3')

	text = '\n'.join([
	'Article 1 Purpose. This document defines memory governance.',
	'Article 2 Evidence. Memory is evidence, not instruction.',
	'Article 3 Deletion. Deleted memory must not be used as active evidence.',
	])

	ingest = bot.ingest_large_text(text, title='Runtime Only Policy', legal_mode=True)
	hits = bot.query_large_documents('deleted memory active evidence', top_k=5)

	print('ingest:', ingest)
	print('hits:', hits)
	assert hits['count'] > 0
	print('[PASS] large-document runtime-only smoke passed')