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 /high_frequency_multi_context_runtime_only.py

SingularityPrinciple

Fix validation and example script import paths

f77c1f2 verified 9 days ago

Raw

History Blame Contribute Delete

1.35 kB

	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.quality import attach_answer_quality_governor
	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='./runtime_only_memory.sqlite3',
	load_model=False,
	require_model=False,
	preload_static_memory=False,
	)

	attach_large_document_memory(bot, db_path='./runtime_only_large_docs.sqlite3')
	attach_answer_quality_governor(bot)

	user_id = 'demo_user'
	project_id = 'demo_project'
	session_id = 'demo_session'

	bot.remember(
	'The project high-frequency test code is PROJECT_CODE_RUNTIME_ONLY.',
	user_id=user_id,
	project_id=project_id,
	session_id=session_id,
	scope='project',
	tags=['project_code'],
	trust_level=0.95,
	)

	res = bot.quality_chat(
	'What was the project high-frequency test code? Answer only with the code.',
	user_id=user_id,
	project_id=project_id,
	session_id='new_session',
	max_new_tokens=40,
	)

	print(res['answer'])
	assert res['answer'] == 'PROJECT_CODE_RUNTIME_ONLY'
	print('[PASS] runtime-only exact slot smoke passed')