Instructions to use blackdaqlabs/MiloX1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use blackdaqlabs/MiloX1 with Transformers:

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

pipe = pipeline("text-generation", model="blackdaqlabs/MiloX1")
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("blackdaqlabs/MiloX1")
model = AutoModelForCausalLM.from_pretrained("blackdaqlabs/MiloX1", 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 blackdaqlabs/MiloX1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "blackdaqlabs/MiloX1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "blackdaqlabs/MiloX1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/blackdaqlabs/MiloX1

SGLang

How to use blackdaqlabs/MiloX1 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 "blackdaqlabs/MiloX1" \
    --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": "blackdaqlabs/MiloX1",
		"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 "blackdaqlabs/MiloX1" \
        --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": "blackdaqlabs/MiloX1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use blackdaqlabs/MiloX1 with Docker Model Runner:
```
docker model run hf.co/blackdaqlabs/MiloX1
```

teszenofficial commited on 2 days ago

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53ce06a

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@@ -1,44 +0,0 @@
----
-language:
-- es
-- en
-library_name: transformers
-pipeline_tag: text-generation
-license: other
-tags:
-- milox
-- from-scratch
-- llama
----
-# MiloX
-**MiloX** es un modelo de lenguaje tipo GPT (Transformer decoder, arquitectura Llama: RMSNorm + RoPE + SwiGLU + GQA)
-de **30M parámetros**, **preentrenado desde cero** (sin pesos de ningún modelo previo) y afinado para conversar.
-Su asistente se llama **Milo**.
-- Contexto: 1024 tokens · Vocabulario: 12288 (BPE byte-level propio) · Idiomas: español e inglés
-- Formato de chat: `<|system|>`, `<|user|>`, `<|assistant|>` terminados en `<|end|>` (ya incluido como `chat_template`)
-## Uso
-```python
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-repo = "TU_USUARIO/MiloX"
-device = "cuda" if torch.cuda.is_available() else "cpu"
-tok = AutoTokenizer.from_pretrained(repo)
-model = AutoModelForCausalLM.from_pretrained(repo).to(device).eval()
-msgs = [{"role": "user", "content": "Hola, ¿quién eres?"}]
-text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
-inputs = tok(text, return_tensors="pt", add_special_tokens=False).to(device)
-out = model.generate(**inputs, max_new_tokens=200, do_sample=True, temperature=0.5,
-                     top_p=0.9, top_k=30)
-print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
-```
-## Limitaciones
-Es un modelo pequeño: puede equivocarse, inventar datos o repetirse. No lo uses para decisiones importantes.
-Revisa las licencias de los datasets usados en el preentrenamiento y el fine-tuning antes de elegir la licencia final del repo.