Instructions to use MonumentalSystems/Ouroboros-1MContext-Gemma-270m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MonumentalSystems/Ouroboros-1MContext-Gemma-270m with Transformers:

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

pipe = pipeline("text-generation", model="MonumentalSystems/Ouroboros-1MContext-Gemma-270m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MonumentalSystems/Ouroboros-1MContext-Gemma-270m")
model = AutoModelForCausalLM.from_pretrained("MonumentalSystems/Ouroboros-1MContext-Gemma-270m", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MonumentalSystems/Ouroboros-1MContext-Gemma-270m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MonumentalSystems/Ouroboros-1MContext-Gemma-270m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MonumentalSystems/Ouroboros-1MContext-Gemma-270m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MonumentalSystems/Ouroboros-1MContext-Gemma-270m

SGLang

How to use MonumentalSystems/Ouroboros-1MContext-Gemma-270m 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 "MonumentalSystems/Ouroboros-1MContext-Gemma-270m" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MonumentalSystems/Ouroboros-1MContext-Gemma-270m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "MonumentalSystems/Ouroboros-1MContext-Gemma-270m" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MonumentalSystems/Ouroboros-1MContext-Gemma-270m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MonumentalSystems/Ouroboros-1MContext-Gemma-270m with Docker Model Runner:
```
docker model run hf.co/MonumentalSystems/Ouroboros-1MContext-Gemma-270m
```

Ouroboros-1MContext-Gemma-270m / README.md

LisaMegaWatts

Add model card with architecture details and LoRA config

09a6480 verified 4 months ago

preview code

Raw

History Blame Contribute Delete

1.5 kB

metadata

language: en
license: apache-2.0
tags:
  - gemma3
  - causal-lm
  - 1m-context
  - symbiogenesis
  - episodic-memory
base_model: google/gemma-3-270m
library_name: transformers

Ouroboros-1MContext-Gemma-270m

1M-context extension of Google's Gemma 3 270M for episodic memory research.

Architecture

Parameter	Value
Model type	Gemma3ForCausalLM
Hidden size	640
Intermediate size	2048
Num layers	18
Num attention heads	4 (GQA, 1 KV head)
Head dim	256
Vocab size	262,144
Max position embeddings	1,048,576 (1M context)
Total parameters	268,098,176
VRAM (bfloat16)	0.54 GB

LoRA Adapter Configuration (for episodic memory)

Parameter	Value
Rank	4
Alpha	8
Target modules	gate_proj, up_proj, down_proj
Params per adapter	580,608
Per LoRA pair (one layer, one module)	10,752

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("MonumentalSystems/Ouroboros-1MContext-Gemma-270m", torch_dtype="bfloat16")
tokenizer = AutoTokenizer.from_pretrained("MonumentalSystems/Ouroboros-1MContext-Gemma-270m")

Citation

Used in "Continuous Memory: Zero-Forgetting Episodic Memory via Per-Memory LoRA Adapters" (ICLR 2026 submission).

W&B runs: 41uzevnn, oa8xe89e, 2v01e4e9 (project: symbiogenesis)

License

Apache 2.0 (following Gemma 3 license)