Libraries Transformers How to use intrect/VELA with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="intrect/VELA")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("intrect/VELA")
model = AutoModelForCausalLM.from_pretrained("intrect/VELA")
messages = [
{"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) llama-cpp-python How to use intrect/VELA with llama-cpp-python:
# !pip install llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="intrect/VELA",
filename="vela-dpo-v6-q4_k_m.gguf",
)
llm.create_chat_completion(
messages = [
{
"role": "user",
"content": "What is the capital of France?"
}
]
) MLX How to use intrect/VELA with MLX:
# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# Generate text with mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("intrect/VELA")
prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
text = generate(model, tokenizer, prompt=prompt, verbose=True) Inference Inference Providers Notebooks Google Colab Kaggle Local Apps llama.cpp How to use intrect/VELA with llama.cpp:
Install from brew brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf intrect/VELA:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf intrect/VELA:Q4_K_M Install from WinGet (Windows) winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf intrect/VELA:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf intrect/VELA:Q4_K_M 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 intrect/VELA:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf intrect/VELA:Q4_K_M 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 intrect/VELA:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf intrect/VELA:Q4_K_M Use Docker docker model run hf.co/intrect/VELA:Q4_K_M LM Studio Jan vLLM How to use intrect/VELA with vLLM:
Install from pip and serve model # Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "intrect/VELA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "intrect/VELA",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}' Use Docker docker model run hf.co/intrect/VELA:Q4_K_M SGLang How to use intrect/VELA 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 "intrect/VELA" \
--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": "intrect/VELA",
"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 "intrect/VELA" \
--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": "intrect/VELA",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}' Ollama How to use intrect/VELA with Ollama:
ollama run hf.co/intrect/VELA:Q4_K_M Unsloth Studio How to use intrect/VELA 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 intrect/VELA 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 intrect/VELA to start chatting Using HuggingFace Spaces for Unsloth # No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for intrect/VELA to start chatting Pi How to use intrect/VELA with Pi:
Start the MLX server # Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "intrect/VELA" Configure the model in Pi # Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
"providers": {
"mlx-lm": {
"baseUrl": "http://localhost:8080/v1",
"api": "openai-completions",
"apiKey": "none",
"models": [
{
"id": "intrect/VELA"
}
]
}
}
} Run Pi # Start Pi in your project directory:
pi Hermes Agent new How to use intrect/VELA with Hermes Agent:
Start the MLX server # Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "intrect/VELA" Configure Hermes # Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default intrect/VELA Run Hermes hermes MLX LM How to use intrect/VELA with MLX LM:
Generate or start a chat session # Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "intrect/VELA" Run an OpenAI-compatible server # Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "intrect/VELA"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "intrect/VELA",
"messages": [
{"role": "user", "content": "Hello"}
]
}' Docker Model Runner How to use intrect/VELA with Docker Model Runner:
docker model run hf.co/intrect/VELA:Q4_K_M Lemonade How to use intrect/VELA with Lemonade:
Pull the model # Download Lemonade from https://lemonade-server.ai/
lemonade pull intrect/VELA:Q4_K_M Run and chat with the model lemonade run user.VELA-Q4_K_M List all available models lemonade list 
