Text Generation
Transformers
TensorBoard
Safetensors
PEFT
Trained with AutoTrain
text-generation-inference
conversational
Instructions to use Cal-mfbc5446/LlmCompiler-Stm32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cal-mfbc5446/LlmCompiler-Stm32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Cal-mfbc5446/LlmCompiler-Stm32") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Cal-mfbc5446/LlmCompiler-Stm32", device_map="auto") - PEFT
How to use Cal-mfbc5446/LlmCompiler-Stm32 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Cal-mfbc5446/LlmCompiler-Stm32 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Cal-mfbc5446/LlmCompiler-Stm32" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Cal-mfbc5446/LlmCompiler-Stm32", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Cal-mfbc5446/LlmCompiler-Stm32
- SGLang
How to use Cal-mfbc5446/LlmCompiler-Stm32 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 "Cal-mfbc5446/LlmCompiler-Stm32" \ --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": "Cal-mfbc5446/LlmCompiler-Stm32", "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 "Cal-mfbc5446/LlmCompiler-Stm32" \ --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": "Cal-mfbc5446/LlmCompiler-Stm32", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Cal-mfbc5446/LlmCompiler-Stm32 with Docker Model Runner:
docker model run hf.co/Cal-mfbc5446/LlmCompiler-Stm32
Upload folder using huggingface_hub
Browse files
runs/Oct30_20-27-55_r-cal-mfbc5446-stf-pfc2-qk1wcp2i-05ae9-txbr7/events.out.tfevents.1761856093.r-cal-mfbc5446-stf-pfc2-qk1wcp2i-05ae9-txbr7.98.0
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