Instructions to use luigicfilho/Numera-v1-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use luigicfilho/Numera-v1-3b with Transformers:

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

pipe = pipeline("text-generation", model="luigicfilho/Numera-v1-3b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("luigicfilho/Numera-v1-3b")
model = AutoModelForCausalLM.from_pretrained("luigicfilho/Numera-v1-3b", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use luigicfilho/Numera-v1-3b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/luigicfilho/Numera-v1-3b

SGLang

How to use luigicfilho/Numera-v1-3b 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 "luigicfilho/Numera-v1-3b" \
    --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": "luigicfilho/Numera-v1-3b",
		"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 "luigicfilho/Numera-v1-3b" \
        --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": "luigicfilho/Numera-v1-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use luigicfilho/Numera-v1-3b with Docker Model Runner:
```
docker model run hf.co/luigicfilho/Numera-v1-3b
```

Numera-v1-3b / README.md

luigicfilho

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	---
	language:
	- en
	license: mit
	library_name: transformers
	tags:
	- generated
	- numerical-generation
	- weight-space
	- text-generation
	inference: true
	model_creator: LCDev-LLMGen
	widget:
	- text: "The nature of mathematics is"
	example_title: "Nature of mathematics"
	- text: "Once upon a time"
	example_title: "Story"
	---

	<div align="center">

	![Numera Banner](Numera.png)

	# Numera: The Numerically Generated Model

	</div>

	Numera-v1 is a 3 Billion parameter transformer-based causal decoder model designed for high-quality text generation and conceptual coherence. It is part of the Numera series focusing on structural integrity and advanced architectural optimization.

	## Model Details

	- Model Name: Numera (Gen-1) 3B
	- Generated By: LCDev-Numera
	- Base Architecture: Qwen2.5
	- Parameters: 3 Billion
	- Type: Statistical Weight Generation
	- Date Generated: 2026-02-20


	### Model Technical Specifications
	Here are the details for Numera (Gen-1) 3b :

	- Total Parameters: ~3.09 Billion
	- Architecture: Qwen2.5 Family
	- Layers: 36
	- Attention Heads: 16 (Query) / 2 (KV - Grouped Query Attention)
	- Hidden Size: 2048
	- Intermediate Size (MLP): 11008
	- Vocab Size: 151,936 tokens
	- Context Window: 32,768 tokens
	- Format: SafeTensors (Universal, safe serialization)
	- Nature: Numerically Generated (Non-trained, statistical approximation)

	## Intended Use

	This model is intended for research into:
	- Weight space analysis of Large Language Models.
	- Statistical properties of model weights.
	- Experimental initialization checkpoints.

	Note: This model is a statistical approximation and not a trained model. It may exhibit repetitive behaviors or lack specific factual knowledge.

	## Usage

	You can use this model with the `transformers` library:

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_name = "luigicfilho/Numera-v1-3b"
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForCausalLM.from_pretrained(model_name)

	prompt = "The nature of mathematics is"
	inputs = tokenizer(prompt, return_tensors="pt")
	outputs = model.generate(**inputs, max_new_tokens=50)
	print(tokenizer.decode(outputs[0], skip_special_tokens=True))
	```

	## License

	This model is released under the MIT License.