Instructions to use XHToken/Spark-X2.6-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use XHToken/Spark-X2.6-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="XHToken/Spark-X2.6-4B", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("XHToken/Spark-X2.6-4B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use XHToken/Spark-X2.6-4B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "XHToken/Spark-X2.6-4B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "XHToken/Spark-X2.6-4B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/XHToken/Spark-X2.6-4B
- SGLang
How to use XHToken/Spark-X2.6-4B 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 "XHToken/Spark-X2.6-4B" \ --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": "XHToken/Spark-X2.6-4B", "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 "XHToken/Spark-X2.6-4B" \ --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": "XHToken/Spark-X2.6-4B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use XHToken/Spark-X2.6-4B with Docker Model Runner:
docker model run hf.co/XHToken/Spark-X2.6-4B
Badass model
People complaining about reasoning verbosity are overlooking the fact that a model this small can even sustain reasoning for that long.
It's a fine model. Just wish it had MTP to make the latency more bearable.
It's a fine model. Just wish it had MTP to make the latency more bearable.
@ManIDontlike working on it, just a few days.
I asked this model to solve a basic mechanics problem involving a cylinder rolling down an inclined plane. It spent 64k tokens reasoning and still failed to produce an answer.
It's impressive that a 4B model can sustain such a long reasoning process, but this also highlights the difference between generating long chains of thought and actually converging on a solution. More reasoning tokens don't necessarily mean better reasoning.