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

Update README.md

4207713 verified 8 months ago

preview code

Raw History Blame Contribute Delete

2.28 kB

metadata

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

Numera: The Numerically Generated Model

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:

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.