Text Generation
Transformers
TensorBoard
Safetensors
gpt_bigcode
Generated from Trainer
text-generation-inference
Instructions to use pierreqi/starcoderbase-1b-cont-scilab-2048 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pierreqi/starcoderbase-1b-cont-scilab-2048 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="pierreqi/starcoderbase-1b-cont-scilab-2048")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("pierreqi/starcoderbase-1b-cont-scilab-2048") model = AutoModelForCausalLM.from_pretrained("pierreqi/starcoderbase-1b-cont-scilab-2048") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use pierreqi/starcoderbase-1b-cont-scilab-2048 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "pierreqi/starcoderbase-1b-cont-scilab-2048" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pierreqi/starcoderbase-1b-cont-scilab-2048", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/pierreqi/starcoderbase-1b-cont-scilab-2048
- SGLang
How to use pierreqi/starcoderbase-1b-cont-scilab-2048 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 "pierreqi/starcoderbase-1b-cont-scilab-2048" \ --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": "pierreqi/starcoderbase-1b-cont-scilab-2048", "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 "pierreqi/starcoderbase-1b-cont-scilab-2048" \ --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": "pierreqi/starcoderbase-1b-cont-scilab-2048", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use pierreqi/starcoderbase-1b-cont-scilab-2048 with Docker Model Runner:
docker model run hf.co/pierreqi/starcoderbase-1b-cont-scilab-2048
- Xet hash:
- f8f80c694352b0012142e78cade1484584b2291a1e95d659c058cf002192744a
- Size of remote file:
- 5.43 kB
- SHA256:
- c2e46392abf17b99890519b75aaa611f377beaef10d5c3efffc211be7322b362
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