Text Generation
Transformers
Safetensors
GGUF
multilingual
English
code
moderato_moe
Mixture of Experts
mixture-of-experts
reflexive-role-routing
code-generation
reasoning
qwen
qwen3_8
qwen3.8
llama.cpp
ollama
conversational
Eval Results
Instructions to use nitrai-research/Moderato-V1-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nitrai-research/Moderato-V1-Pro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nitrai-research/Moderato-V1-Pro") 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("nitrai-research/Moderato-V1-Pro", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nitrai-research/Moderato-V1-Pro with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nitrai-research/Moderato-V1-Pro" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nitrai-research/Moderato-V1-Pro", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/nitrai-research/Moderato-V1-Pro
- SGLang
How to use nitrai-research/Moderato-V1-Pro 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 "nitrai-research/Moderato-V1-Pro" \ --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": "nitrai-research/Moderato-V1-Pro", "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 "nitrai-research/Moderato-V1-Pro" \ --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": "nitrai-research/Moderato-V1-Pro", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use nitrai-research/Moderato-V1-Pro with Docker Model Runner:
docker model run hf.co/nitrai-research/Moderato-V1-Pro
Commit History
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Add community evaluation results for DEEP-SWE, GPQA, HLE, SWE-BENCH_PRO, TERMINAL-BENCH, WILDCLAWBENCH, EXTRACTBENCH (#3)
- Add community evaluation results for DEEP-SWE, GPQA, HLE, SWE-BENCH_PRO, TERMINAL-BENCH, WILDCLAWBENCH, EXTRACTBENCH (c1c2db1b8793e9a692844c29186c5f8a83c906d1) Co-authored-by: NitrAI Bot <NitrAI-BOT@users.noreply.huggingface.co>560ead4 -
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