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
```

GiuliannoV commited on 7 days ago

Commit

fb8a64f

verified ·

1 Parent(s): 4d85c42

Update benchmark_suite_4k.json with latest base model evaluation scores

Browse files

Files changed (1) hide show

benchmark_suite_4k.json +2 -2

benchmark_suite_4k.json CHANGED Viewed

@@ -185,7 +185,7 @@
    "Granite 4.1 8B": "4.91",
    "Ministral 3 8B": "8.14",
    "Olmo 3 7B": "7.65",
-   "Falcon-H1 7B": "5.38",
    "Qwen3.5 4B": "2.17",
    "Qwen3 4B": "7.92",
    "Gemma 4 E4B": "2.19"
@@ -1043,7 +1043,7 @@
    "Granite 4.0 H Tiny": "62.90",
    "Trinity Nano": "58.66",
    "OLMoE 1B-7B": "50.87",
-   "Qwen3.5 9B": "0.00",
    "Qwen3 8B": "58.96",
    "Gemma 4 12B": "63.08",
    "Granite 4.1 8B": "71.68",

    "Granite 4.1 8B": "4.91",
    "Ministral 3 8B": "8.14",
    "Olmo 3 7B": "7.65",
+   "Falcon-H1 7B": "5.31",
    "Qwen3.5 4B": "2.17",
    "Qwen3 4B": "7.92",
    "Gemma 4 E4B": "2.19"
    "Granite 4.0 H Tiny": "62.90",
    "Trinity Nano": "58.66",
    "OLMoE 1B-7B": "50.87",
+   "Qwen3.5 9B": "pending",
    "Qwen3 8B": "58.96",
    "Gemma 4 12B": "63.08",
    "Granite 4.1 8B": "71.68",