Text Generation
Transformers
Safetensors
starcoder2
fp8
vllm
text-generation-inference
compressed-tensors
Instructions to use RedHatAI/starcoder2-7b-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RedHatAI/starcoder2-7b-FP8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RedHatAI/starcoder2-7b-FP8")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RedHatAI/starcoder2-7b-FP8") model = AutoModelForCausalLM.from_pretrained("RedHatAI/starcoder2-7b-FP8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use RedHatAI/starcoder2-7b-FP8 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RedHatAI/starcoder2-7b-FP8" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RedHatAI/starcoder2-7b-FP8", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/RedHatAI/starcoder2-7b-FP8
- SGLang
How to use RedHatAI/starcoder2-7b-FP8 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 "RedHatAI/starcoder2-7b-FP8" \ --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": "RedHatAI/starcoder2-7b-FP8", "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 "RedHatAI/starcoder2-7b-FP8" \ --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": "RedHatAI/starcoder2-7b-FP8", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use RedHatAI/starcoder2-7b-FP8 with Docker Model Runner:
docker model run hf.co/RedHatAI/starcoder2-7b-FP8
Download model-00002-of-00002.safetensors from RedHatAI/starcoder2-7b-FP8: direct link, hf CLI and curl.
- Browser
- Download file 2.93 GB
-
https://huggingface.co/RedHatAI/starcoder2-7b-FP8/resolve/main/model-00002-of-00002.safetensors
- Command line
-
hf download hf://RedHatAI/starcoder2-7b-FP8/model-00002-of-00002.safetensors
-
curl -L -o model-00002-of-00002.safetensors https://huggingface.co/RedHatAI/starcoder2-7b-FP8/resolve/main/model-00002-of-00002.safetensors
2.93 GB
- Xet hash:
- 2eb619c1a69efca98b5f4ab7f692a5f601fb72539d0b04b627d021e2471399fe
- Size of remote file:
- 2.93 GB
- SHA256:
- e394c31eee2e6f0e2651750013dc09dd27bcdc8165dc487bf64c242c5379d326
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