Instructions to use Lernex/Metis-1.6-Logos-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lernex/Metis-1.6-Logos-Base with Transformers:

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

pipe = pipeline("text-generation", model="Lernex/Metis-1.6-Logos-Base", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Lernex/Metis-1.6-Logos-Base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Lernex/Metis-1.6-Logos-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lernex/Metis-1.6-Logos-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lernex/Metis-1.6-Logos-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Lernex/Metis-1.6-Logos-Base

SGLang

How to use Lernex/Metis-1.6-Logos-Base 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 "Lernex/Metis-1.6-Logos-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lernex/Metis-1.6-Logos-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Lernex/Metis-1.6-Logos-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lernex/Metis-1.6-Logos-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Lernex/Metis-1.6-Logos-Base with Docker Model Runner:
```
docker model run hf.co/Lernex/Metis-1.6-Logos-Base
```

Metis-1.6-Logos-Base / NGRAM_CANONICAL_IDS.json

GiuliannoV

Metis-1.6 Logos Base: final pretraining checkpoint at 804,755,808,835 tokens (exporter 7bb7c93)

dbe8f9c 24 days ago

Raw History Blame Contribute Delete

1.1 kB

{
  "algorithm": "deepseek-engram-normalization/v1",
  "algorithm_reference": "https://github.com/deepseek-ai/Engram/blob/main/engram_demo_v1.py",
  "binary": "NGRAM_CANONICAL_IDS.uint16",
  "binary_sha256": "6359312eb0b5e892828430d2c9d049021f452580f724265f2f21003565a732a3",
  "binary_size_bytes": 131072,
  "canonical_ids_contiguous": true,
  "canonical_vocabulary_size": 39789,
  "created_at": "2026-08-15T05:26:32.143022+00:00",
  "dtype": "uint16",
  "empty_normalization_fallback": "decoded_token",
  "endianness": "little",
  "entry_count": 65536,
  "manifest_sha256": "0d502c64584cf435bfff6f244ff1dc7d879ca120cd8b060c7616d6dc453c4101",
  "maximum_canonical_id": 39788,
  "minimum_canonical_id": 0,
  "normalization_steps": [
    "NFKC",
    "NFD",
    "StripAccents",
    "Lowercase",
    "CollapseAsciiWhitespace",
    "PreserveSingleSpace",
    "Strip"
  ],
  "replacement_character_fallback": "raw_tokenizer_vocab_token",
  "schema": "metis.ngram-canonical-ids/v1",
  "tokenizer_sha256": "2304b0e1c7b1ff18a3797c40d035bc781652485db4f1fc89381bfde44bbfc76c",
  "vocabulary_size": 65536
}

	{
	"algorithm": "deepseek-engram-normalization/v1",
	"algorithm_reference": "https://github.com/deepseek-ai/Engram/blob/main/engram_demo_v1.py",
	"binary": "NGRAM_CANONICAL_IDS.uint16",
	"binary_sha256": "6359312eb0b5e892828430d2c9d049021f452580f724265f2f21003565a732a3",
	"binary_size_bytes": 131072,
	"canonical_ids_contiguous": true,
	"canonical_vocabulary_size": 39789,
	"created_at": "2026-08-15T05:26:32.143022+00:00",
	"dtype": "uint16",
	"empty_normalization_fallback": "decoded_token",
	"endianness": "little",
	"entry_count": 65536,
	"manifest_sha256": "0d502c64584cf435bfff6f244ff1dc7d879ca120cd8b060c7616d6dc453c4101",
	"maximum_canonical_id": 39788,
	"minimum_canonical_id": 0,
	"normalization_steps": [
	"NFKC",
	"NFD",
	"StripAccents",
	"Lowercase",
	"CollapseAsciiWhitespace",
	"PreserveSingleSpace",
	"Strip"
	],
	"replacement_character_fallback": "raw_tokenizer_vocab_token",
	"schema": "metis.ngram-canonical-ids/v1",
	"tokenizer_sha256": "2304b0e1c7b1ff18a3797c40d035bc781652485db4f1fc89381bfde44bbfc76c",
	"vocabulary_size": 65536
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