Instructions to use keyfan/grok-1-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use keyfan/grok-1-hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="keyfan/grok-1-hf", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("keyfan/grok-1-hf", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use keyfan/grok-1-hf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "keyfan/grok-1-hf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "keyfan/grok-1-hf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/keyfan/grok-1-hf
- SGLang
How to use keyfan/grok-1-hf 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 "keyfan/grok-1-hf" \ --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": "keyfan/grok-1-hf", "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 "keyfan/grok-1-hf" \ --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": "keyfan/grok-1-hf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use keyfan/grok-1-hf with Docker Model Runner:
docker model run hf.co/keyfan/grok-1-hf
Unofficial dequantized weight of grok-1 in HF Transformers format.
Note: If you haven't download the weight yet, please use the fp32 revision instead which uses float32 precision for RMSNorm and Router layer for better consistency.
The (fp32) weights are converted using the script here ran inside the grok-1 repo. Since downloading the dequantized weight needs twice as much time, it's recommended to download the original weight and convert on your own.
Benchmarks
(I ran with load_in_8bit using lm-evaluation-harness due to limited hardware, so the result will be slightly worse)
- MMLU 5-shot: 0.7166
- BBH 3-shot: 0.5204
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