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 25 days ago

Commit

51e2ce4

verified ·

1 Parent(s): 7848c1e

Upload 4 files

Browse files

Files changed (2) hide show

README.md +25 -0
config.json +34 -0

README.md ADDED Viewed

	@@ -0,0 +1,25 @@

+---
+license: mit
+language: es
+tags:
+- milo-x1
+- from-scratch-transformer
+- spanish
+---
+# Milo X1 (fine-tuned)
+Modelo de lenguaje Transformer autoregresivo en español, entrenado completamente desde cero
+(sin pesos preentrenados, arquitectura propia con RoPE + QK-Norm + atención causal + SwiGLU + RMSNorm).
+- Parámetros: ~13.47M
+- d_model: 256 | capas: 6 | heads: 8
+- context_length: 768
+- Vocabulario: 14016 tokens en el tokenizer (14016 filas de embedding)
+- Tokenizer: BPE byte-level (librería `tokenizers`), normalización NFC+Replace;
+  codifica y decodifica sin pérdida cualquier texto Unicode (acentos, símbolos, emojis).
+## Archivos
+- `model.safetensors`: pesos del modelo
+- `config.json`: hiperparámetros de arquitectura (para reconstruir `MiloX1`)
+- `tokenizer.json`: tokenizer (formato nativo de la librería `tokenizers`; incluye normalizador y pre-tokenizador)

config.json ADDED Viewed

	@@ -0,0 +1,34 @@

+{
+  "vocab_size": 14016,
+  "d_model": 256,
+  "n_layers": 6,
+  "n_heads": 8,
+  "d_ff": 1024,
+  "context_length": 768,
+  "dropout": 0.1,
+  "rope_theta": 10000.0,
+  "qk_norm": true,
+  "final_logit_softcap": 30.0,
+  "label_smoothing": 0.0,
+  "architecture": "MiloX1",
+  "variant": "fine-tuned",
+  "tokenizer_info": {
+    "type": "bpe-byte-level",
+    "vocab_size": 14016,
+    "normalizer": "NFC+Replace",
+    "special_tokens": [
+      "<PAD>",
+      "<UNK>",
+      "<BOS>",
+      "<EOS>",
+      "<|reserved_0|>",
+      "<|reserved_1|>",
+      "<|reserved_2|>",
+      "<|reserved_3|>",
+      "<|reserved_4|>",
+      "<|reserved_5|>",
+      "<|reserved_6|>",
+      "<|reserved_7|>"
+    ]
+  }
+}