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
```

luigicfilho commited on Feb 21

Commit

1842ad9

verified ·

1 Parent(s): 92fa965

add model info readme

Browse files

Files changed (1) hide show

README.md +82 -3

README.md CHANGED Viewed

@@ -1,3 +1,82 @@
----
-license: mit
----

+---
+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)
+- **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.