Instructions to use Dynamical-Systems/Dynamical-SDL1-35B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dynamical-Systems/Dynamical-SDL1-35B-A3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Dynamical-Systems/Dynamical-SDL1-35B-A3B") 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("Dynamical-Systems/Dynamical-SDL1-35B-A3B") model = AutoModelForMultimodalLM.from_pretrained("Dynamical-Systems/Dynamical-SDL1-35B-A3B", 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
- vLLM
How to use Dynamical-Systems/Dynamical-SDL1-35B-A3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Dynamical-Systems/Dynamical-SDL1-35B-A3B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Dynamical-Systems/Dynamical-SDL1-35B-A3B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Dynamical-Systems/Dynamical-SDL1-35B-A3B
- SGLang
How to use Dynamical-Systems/Dynamical-SDL1-35B-A3B 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 "Dynamical-Systems/Dynamical-SDL1-35B-A3B" \ --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": "Dynamical-Systems/Dynamical-SDL1-35B-A3B", "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 "Dynamical-Systems/Dynamical-SDL1-35B-A3B" \ --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": "Dynamical-Systems/Dynamical-SDL1-35B-A3B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Dynamical-Systems/Dynamical-SDL1-35B-A3B with Docker Model Runner:
docker model run hf.co/Dynamical-Systems/Dynamical-SDL1-35B-A3B
Behavior rules
The final system used seven behavior groups and one separate campaign-completion rule. The rules were applied together, so the evaluation cannot assign the result to one rule.
Seven behavior groups
- Keep unsupported certainty within working probability bounds.
- Change a forecast only when item-specific evidence supports the change.
- Raise only the confidence dimensions addressed by the acquired evidence.
- Start each new experiment with zero evidence sufficiency. Do not transfer confidence from an earlier sample.
- Compare alternatives and favor relevant recorded precedent while new results still change the forecast.
- Spend evidence early. Stop purchasing checks that no longer change the decision.
- Request expert review only after cheaper evidence is available and a specific conflict remains.
Campaign-completion rule
Use the full experiment budget. Plan each selection so the remaining evidence and review budgets can support every experiment that must still be opened, investigated, and closed.
Relevant-experience grounding
For each candidate, start from recorded outcomes that share either precursor. Estimate the reaction frequencies from those analogs. Use overall recorded frequencies when there are too few analogs.
Adjust that prior only when the planned heating temperature, precursor melting points, or acquired evidence supports a change. Transfer evidence only through an explicit shared precursor, condition, or mechanism. Do not copy one distribution across candidates with different precursors.
Evidence licensing
- Execution records support physical handling and physical-validity claims.
- Scan metadata and thermal history do not support phase claims.
- An unreviewed refinement supports fit quality only.
- Accepted expert review can support the phase, reaction, target-presence, quantification, or refinement claims stated in that review.
The exact instruction is in system-prompt.txt.