Instructions to use Ilides/cortex-v0.9-0.1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ilides/cortex-v0.9-0.1b with Transformers:

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

pipe = pipeline("text-generation", model="Ilides/cortex-v0.9-0.1b", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Ilides/cortex-v0.9-0.1b", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ilides/cortex-v0.9-0.1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ilides/cortex-v0.9-0.1b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ilides/cortex-v0.9-0.1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ilides/cortex-v0.9-0.1b

SGLang

How to use Ilides/cortex-v0.9-0.1b 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 "Ilides/cortex-v0.9-0.1b" \
    --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": "Ilides/cortex-v0.9-0.1b",
		"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 "Ilides/cortex-v0.9-0.1b" \
        --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": "Ilides/cortex-v0.9-0.1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Ilides/cortex-v0.9-0.1b with Docker Model Runner:
```
docker model run hf.co/Ilides/cortex-v0.9-0.1b
```

cortex-v0.9-0.1b / configuration_cortex.py

Ilides

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"""Configuración de Cortex-1 para Hugging Face transformers (trust_remote_code)."""
from transformers import PretrainedConfig


class CortexConfig(PretrainedConfig):
    model_type = "cortex"

    def __init__(
        self,
        vocab_size=16384,
        hidden_size=384,
        num_hidden_layers=6,
        num_attention_heads=8,
        intermediate_size=1536,
        max_position_embeddings=512,
        rms_norm_eps=1e-5,
        initializer_std=0.02,
        tie_word_embeddings=True,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.max_position_embeddings = max_position_embeddings
        self.rms_norm_eps = rms_norm_eps
        self.initializer_std = initializer_std
        self.tie_word_embeddings = tie_word_embeddings

	"""Configuración de Cortex-1 para Hugging Face transformers (trust_remote_code)."""
	from transformers import PretrainedConfig


	class CortexConfig(PretrainedConfig):
	model_type = "cortex"

	def __init__(
	self,
	vocab_size=16384,
	hidden_size=384,
	num_hidden_layers=6,
	num_attention_heads=8,
	intermediate_size=1536,
	max_position_embeddings=512,
	rms_norm_eps=1e-5,
	initializer_std=0.02,
	tie_word_embeddings=True,
	**kwargs,
	):
	super().__init__(**kwargs)
	self.vocab_size = vocab_size
	self.hidden_size = hidden_size
	self.num_hidden_layers = num_hidden_layers
	self.num_attention_heads = num_attention_heads
	self.intermediate_size = intermediate_size
	self.max_position_embeddings = max_position_embeddings
	self.rms_norm_eps = rms_norm_eps
	self.initializer_std = initializer_std
	self.tie_word_embeddings = tie_word_embeddings