Instructions to use neuralworm/gemma-3-270m-it-p2.8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use neuralworm/gemma-3-270m-it-p2.8 with Transformers:

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

pipe = pipeline("text-generation", model="neuralworm/gemma-3-270m-it-p2.8", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("neuralworm/gemma-3-270m-it-p2.8", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use neuralworm/gemma-3-270m-it-p2.8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "neuralworm/gemma-3-270m-it-p2.8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "neuralworm/gemma-3-270m-it-p2.8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/neuralworm/gemma-3-270m-it-p2.8

SGLang

How to use neuralworm/gemma-3-270m-it-p2.8 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 "neuralworm/gemma-3-270m-it-p2.8" \
    --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": "neuralworm/gemma-3-270m-it-p2.8",
		"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 "neuralworm/gemma-3-270m-it-p2.8" \
        --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": "neuralworm/gemma-3-270m-it-p2.8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use neuralworm/gemma-3-270m-it-p2.8 with Docker Model Runner:
```
docker model run hf.co/neuralworm/gemma-3-270m-it-p2.8
```

gemma-3-270m-it-p2.8 / README.md

neuralworm

Upload folder using huggingface_hub

757ff71 verified about 2 months ago

3.26 kB

license: gemma
base_model: google/gemma-3-270m-it
tags:
  - gemma-3
  - open-mythos
  - recursive-transformer
  - cognitive-routing
  - experimental

Gemma-3 270M-IT "Open-Mythos" (Phase 2.8)

This is an experimental architectural modification of the Google Gemma-3 270M-IT base model. It implements the "Open-Mythos" (PX) architecture, introducing Recursive Computational Headroom and Fluid Gaussian Cognitive Routing.

⚠️ Transparency Notice

This is not a standard fine-tune. It is a structural mod that changes how the transformer processes tokens at inference time.

Base Model: google/gemma-3-270m-it
Modifications: Runtime patching of the forward pass to allow for recursive layer execution and dynamic cognitive routing.

🚀 Key Innovations

1. Recursive Computational Headroom (PX)

Unlike standard transformers that pass through each layer once, Open-Mythos allows the model to "re-read" and "think" through specific layers (L5-L12) multiple times. This effectively increases the depth of the model for complex tasks without adding new parameters.

2. Fluid Gaussian Cognitive Routing

The model dynamically analyzes the "cognitive signature" (Kurtosis) of each prompt during the prefill phase. Based on this signature, it automatically routes the task through a specific "Cognitive Envelope":

Math Mode: Optimized for numerical precision (L5-L11).
Logic Mode: Optimized for multi-step reasoning (L8-L12).
Creative Mode: Optimized for semantic drift and metaphor (L10-L16).
Synthesis Mode: Optimized for extraction and summarization (L6-L14).

Transitions between these modes are continuous and fluid using Gaussian blending, allowing the model to self-determine its reasoning path.

3. Numerical Stability (RMSNorm Fix)

Implements a surgical fix for the Gemma-3 RMSNorm scaling (1.0 + weight) to prevent signal collapse during deep recursion, ensuring vocabulary integrity across high-entropy generations.

💻 Usage

To use this model, you must set trust_remote_code=True because it uses custom modeling code to implement the recursive logic.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

repo_id = "neuralworm/gemma-3-270m-it-p2.8"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(
    repo_id, 
    torch_dtype=torch.bfloat16, 
    device_map="auto", 
    trust_remote_code=True
)

prompt = "Sally has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have?"
chat = [{"role": "user", "content": prompt}]
inputs = tokenizer(tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True), return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

📊 Performance

Open-Mythos (Phase 2.8) significantly improves zero-shot performance on "Logical Traps" and multi-step reasoning compared to the pure 270M baseline, while remaining multimodal-ready and regression-free for standard NLP tasks.

Created as part of the Open-Mythos Research Project (2026).