Text Generation
Transformers
Safetensors
metis16
metis
post-trained
instruction-tuned
reasoning
mixture-of-experts
mamba
conversational
custom_code
Instructions to use Lernex/Metis-1.6-Praxis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lernex/Metis-1.6-Praxis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Lernex/Metis-1.6-Praxis", 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("Lernex/Metis-1.6-Praxis", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Lernex/Metis-1.6-Praxis 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-Praxis" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Lernex/Metis-1.6-Praxis", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Lernex/Metis-1.6-Praxis
- SGLang
How to use Lernex/Metis-1.6-Praxis 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-Praxis" \ --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": "Lernex/Metis-1.6-Praxis", "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 "Lernex/Metis-1.6-Praxis" \ --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": "Lernex/Metis-1.6-Praxis", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Lernex/Metis-1.6-Praxis with Docker Model Runner:
docker model run hf.co/Lernex/Metis-1.6-Praxis
Download NGRAM_MEMORY_REMOVED.json from Lernex/Metis-1.6-Praxis: direct link, hf CLI and curl.
- Browser
- Download file 2.64 kB
-
https://huggingface.co/Lernex/Metis-1.6-Praxis/resolve/main/NGRAM_MEMORY_REMOVED.json
- Command line
-
hf download hf://Lernex/Metis-1.6-Praxis/NGRAM_MEMORY_REMOVED.json
-
curl -L -o NGRAM_MEMORY_REMOVED.json https://huggingface.co/Lernex/Metis-1.6-Praxis/resolve/main/NGRAM_MEMORY_REMOVED.json
2.64 kB
| { | |
| "removed": "ngram_memory.*: hashed tables, projection and gates", | |
| "source": "/lus/lustre1/vollmerc/metis-1.6/exports/Metis-1.6-Praxis-G80-Full", | |
| "source_model_safetensors_sha256": "9654d9c86a51215eb15b6fb126c41cfe2cd5a5c0446478a458693735205b83b4", | |
| "model_safetensors_sha256": "861af5dbaa43923cef0ea4abd71124148ff3078370218fb12d4685fcd1779072", | |
| "removed_tensors": [ | |
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| 2048 | |
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| { | |
| "name": "ngram_memory.projection.bias", | |
| "shape": [ | |
| 2048 | |
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| { | |
| "name": "ngram_memory.projection.weight", | |
| "shape": [ | |
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| "shape": [ | |
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| { | |
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| { | |
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| "shape": [ | |
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| 64 | |
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| { | |
| "name": "ngram_memory.tables.o3_h3.embedding.weight", | |
| "shape": [ | |
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| 64 | |
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| { | |
| "name": "ngram_memory.tables.o3_h4.embedding.weight", | |
| "shape": [ | |
| 589993, | |
| 64 | |
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| { | |
| "name": "ngram_memory.tables.o3_h5.embedding.weight", | |
| "shape": [ | |
| 590959, | |
| 64 | |
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| { | |
| "name": "ngram_memory.tables.o3_h6.embedding.weight", | |
| "shape": [ | |
| 591937, | |
| 64 | |
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| { | |
| "name": "ngram_memory.tables.o3_h7.embedding.weight", | |
| "shape": [ | |
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| "removed_elements": 602180520, | |
| "kept_tensors": 418, | |
| "load_with": "ngram_free.without_ngram_memory()", | |
| "note": "Lernex/Metis-1.6-Praxis: Post-trained overall_sft G80 checkpoint tokens-1022774982313 (8e7512a4), source model.safetensors 9654d9c8" | |
| } | |