Instructions to use haffner/Salience-1-9B-Heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use haffner/Salience-1-9B-Heretic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="haffner/Salience-1-9B-Heretic") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("haffner/Salience-1-9B-Heretic") model = AutoModelForMultimodalLM.from_pretrained("haffner/Salience-1-9B-Heretic", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use haffner/Salience-1-9B-Heretic 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 haffner/Salience-1-9B-Heretic:BF16 # Run inference directly in the terminal: llama cli -hf haffner/Salience-1-9B-Heretic:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf haffner/Salience-1-9B-Heretic:BF16 # Run inference directly in the terminal: llama cli -hf haffner/Salience-1-9B-Heretic:BF16
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 haffner/Salience-1-9B-Heretic:BF16 # Run inference directly in the terminal: ./llama-cli -hf haffner/Salience-1-9B-Heretic:BF16
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 haffner/Salience-1-9B-Heretic:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf haffner/Salience-1-9B-Heretic:BF16
Use Docker
docker model run hf.co/haffner/Salience-1-9B-Heretic:BF16
- LM Studio
- Jan
- vLLM
How to use haffner/Salience-1-9B-Heretic with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "haffner/Salience-1-9B-Heretic" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "haffner/Salience-1-9B-Heretic", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/haffner/Salience-1-9B-Heretic:BF16
- SGLang
How to use haffner/Salience-1-9B-Heretic 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 "haffner/Salience-1-9B-Heretic" \ --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": "haffner/Salience-1-9B-Heretic", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "haffner/Salience-1-9B-Heretic" \ --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": "haffner/Salience-1-9B-Heretic", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Ollama
How to use haffner/Salience-1-9B-Heretic with Ollama:
ollama run hf.co/haffner/Salience-1-9B-Heretic:BF16
- Unsloth Desktop
- Pi
How to use haffner/Salience-1-9B-Heretic with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf haffner/Salience-1-9B-Heretic:BF16
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "haffner/Salience-1-9B-Heretic:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use haffner/Salience-1-9B-Heretic with Docker Model Runner:
docker model run hf.co/haffner/Salience-1-9B-Heretic:BF16
- Lemonade
How to use haffner/Salience-1-9B-Heretic with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull haffner/Salience-1-9B-Heretic:BF16
Run and chat with the model
lemonade run user.Salience-1-9B-Heretic-BF16
List all available models
lemonade list
- Hermes Agent
How to use haffner/Salience-1-9B-Heretic with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf haffner/Salience-1-9B-Heretic:BF16
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 haffner/Salience-1-9B-Heretic:BF16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use haffner/Salience-1-9B-Heretic with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf haffner/Salience-1-9B-Heretic:BF16
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "haffner/Salience-1-9B-Heretic:BF16" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
@haffner Thanks.
@haffner You are the first person to create a heretic version of our models. Thank you! What did you find in our models that pushed you to make heretic variants?
Answered in the other model's thread already. But I'm looking forward to trying this one - and maybe vl-1-coder to see if it can help improve my frontend work π
But currently I'm more into gamedev / system level C development and am thinking to train/fine-tune a model on a very specific dataset that I would have to prep first. Sadly I don't have any experience with this process yet.
We think that for the frontend you can get more satisfaction from Salience, thanks to the vision, and further training on VL-1-Coder data and a better dataset
Oh, I'm from Vectionlabs' team
@haffner Will you also be making the Heretic models from the Salience-1.5 family? Check our post if you want!
@haffner Will you also be making the Heretic models from the Salience-1.5 family? Check our post if you want!
I could give it a shot, but I think they will no longer fit in my vram at which point I can't because it would take forever. It needs to fit the entire safetensors fully into vram (maybe with bnb_4bit). But if you're interested in having these available, have a look here:
https://github.com/p-e-w/heretic
It's a surprisingly simple process. Previously I've run 5000 iterations, but usually you'll have a very decent result within the first 500 trials already. Also depends a bit on the underlying model architecture, of course.