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

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	# Gemma-3 270M-IT-PX (Phase 2.8)

	This is an experimental architectural modification of the Google Gemma-3 270M-IT base model. It implements the PX (Recursive Computational Headroom) architecture 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](https://huggingface.co/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, Gemma-3-PX allows the model to "re-read" and "think" through specific layers 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 and automatically routes the task through a specific "Cognitive Envelope":
	- Math Mode: Optimized for numerical precision.
	- Logic Mode: Optimized for multi-step reasoning.
	- Creative Mode: Optimized for semantic drift and metaphor.
	- Synthesis Mode: Optimized for extraction and summarization.

	## 💻 Usage

	To use this model, you must set `trust_remote_code=True`.

	```python
	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))
	```

	---
	Developed by neuralworm (2026).