Instructions to use RobiLabs/Vera with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RobiLabs/Vera with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RobiLabs/Vera", device_map="auto")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("RobiLabs/Vera", dtype="auto", device_map="auto") - llama-cpp-python
How to use RobiLabs/Vera with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="RobiLabs/Vera", filename="vera_veramoelite_q4.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use RobiLabs/Vera with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf RobiLabs/Vera # Run inference directly in the terminal: llama cli -hf RobiLabs/Vera
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf RobiLabs/Vera # Run inference directly in the terminal: llama cli -hf RobiLabs/Vera
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf RobiLabs/Vera # Run inference directly in the terminal: ./llama-cli -hf RobiLabs/Vera
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf RobiLabs/Vera # Run inference directly in the terminal: ./build/bin/llama-cli -hf RobiLabs/Vera
Use Docker
docker model run hf.co/RobiLabs/Vera
- LM Studio
- Jan
- vLLM
How to use RobiLabs/Vera with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RobiLabs/Vera" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RobiLabs/Vera", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/RobiLabs/Vera
- SGLang
How to use RobiLabs/Vera 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 "RobiLabs/Vera" \ --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": "RobiLabs/Vera", "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 "RobiLabs/Vera" \ --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": "RobiLabs/Vera", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use RobiLabs/Vera with Ollama:
ollama run hf.co/RobiLabs/Vera
- Unsloth Studio
How to use RobiLabs/Vera with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for RobiLabs/Vera to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for RobiLabs/Vera to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for RobiLabs/Vera to start chatting
- Atomic Chat new
- Docker Model Runner
How to use RobiLabs/Vera with Docker Model Runner:
docker model run hf.co/RobiLabs/Vera
- Lemonade
How to use RobiLabs/Vera with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RobiLabs/Vera
Run and chat with the model
lemonade run user.Vera-{{QUANT_TAG}}List all available models
lemonade list
Vera
Vera is a 30B-A3B Mixture-of-Experts (MoE) causal language model developed by Robi Labs. Designed for high performance and lightweight deployment, Vera offers deep conversational capability, software engineering skills, and reasoning support.
Model Summary
- Developed by: Robi Labs
- Model Type: Mixture-of-Experts (MoE) Causal LLM (
VeraMoeLiteForCausalLM) - Languages: English (en)
- License: MIT
- Context Length: 200k tokens
Performances on Benchmarks
| Benchmark | Vera-30B-A3B-MoE | Qwen3-30B-A3B-Thinking-2507 | GPT-OSS-20B |
|---|---|---|---|
| AIME 25 | 91.6 | 85.0 | 91.7 |
| GPQA | 75.2 | 73.4 | 71.5 |
| LCB v6 | 64.0 | 66.0 | 61.0 |
| HLE | 14.4 | 9.8 | 10.9 |
| SWE-bench Verified | 59.2 | 22.0 | 34.0 |
| τ²-Bench | 79.5 | 49.0 | 47.7 |
| BrowseComp | 42.8 | 2.29 | 28.3 |
Key Features
- Architectural Efficiency: Built on a custom MoE architecture (
veramoelite) balancing fast inference times with expert layer routing. - Extended Context: Supports up to 200,000 tokens context window for extensive reasoning, document understanding, and multi-turn conversations.
- Task Capability: Highly optimized for general reasoning, mathematical evaluation, and multi-turn chat alignment.
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