Text Generation
Transformers
Safetensors
gpt_bigcode
Generated from Trainer
text-generation-inference
Instructions to use RafaelZequeira/starcoderbase-1b-cucumber-copilot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RafaelZequeira/starcoderbase-1b-cucumber-copilot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RafaelZequeira/starcoderbase-1b-cucumber-copilot")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RafaelZequeira/starcoderbase-1b-cucumber-copilot") model = AutoModelForCausalLM.from_pretrained("RafaelZequeira/starcoderbase-1b-cucumber-copilot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use RafaelZequeira/starcoderbase-1b-cucumber-copilot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RafaelZequeira/starcoderbase-1b-cucumber-copilot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RafaelZequeira/starcoderbase-1b-cucumber-copilot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/RafaelZequeira/starcoderbase-1b-cucumber-copilot
- SGLang
How to use RafaelZequeira/starcoderbase-1b-cucumber-copilot 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 "RafaelZequeira/starcoderbase-1b-cucumber-copilot" \ --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": "RafaelZequeira/starcoderbase-1b-cucumber-copilot", "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 "RafaelZequeira/starcoderbase-1b-cucumber-copilot" \ --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": "RafaelZequeira/starcoderbase-1b-cucumber-copilot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use RafaelZequeira/starcoderbase-1b-cucumber-copilot with Docker Model Runner:
docker model run hf.co/RafaelZequeira/starcoderbase-1b-cucumber-copilot
Download model.safetensors from RafaelZequeira/starcoderbase-1b-cucumber-copilot: direct link, hf CLI and curl.
- Browser
- Download file 2.48 GB
-
https://huggingface.co/RafaelZequeira/starcoderbase-1b-cucumber-copilot/resolve/main/model.safetensors
- Command line
-
hf download hf://RafaelZequeira/starcoderbase-1b-cucumber-copilot/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/RafaelZequeira/starcoderbase-1b-cucumber-copilot/resolve/main/model.safetensors
2.48 GB
- Xet hash:
- cfff5e735e992cad407aecedea5a23a8a48b1bcb02f3832783dc24a9d7e20d67
- Size of remote file:
- 2.48 GB
- SHA256:
- 56ccba6694407d25804bcd82190f92d645faf3efad54f75fe8a797bc0b177ff9
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