Instructions to use RobiLabs/Vera with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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")

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("RobiLabs/Vera", device_map="auto")

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

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

Atomic Chat

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.

Downloads last month: 364

GGUF

Model size

30B params

Architecture

veramoelite

Hardware compatibility

We're not able to determine the quantization variants.

View all variants