Text Generation
Transformers
PyTorch
Safetensors
English
gpt_neox
causal-lm
text-generation-inference
Instructions to use vvsotnikov/stablelm-tuned-alpha-3b-16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vvsotnikov/stablelm-tuned-alpha-3b-16bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="vvsotnikov/stablelm-tuned-alpha-3b-16bit")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("vvsotnikov/stablelm-tuned-alpha-3b-16bit") model = AutoModelForCausalLM.from_pretrained("vvsotnikov/stablelm-tuned-alpha-3b-16bit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use vvsotnikov/stablelm-tuned-alpha-3b-16bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "vvsotnikov/stablelm-tuned-alpha-3b-16bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "vvsotnikov/stablelm-tuned-alpha-3b-16bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/vvsotnikov/stablelm-tuned-alpha-3b-16bit
- SGLang
How to use vvsotnikov/stablelm-tuned-alpha-3b-16bit 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 "vvsotnikov/stablelm-tuned-alpha-3b-16bit" \ --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": "vvsotnikov/stablelm-tuned-alpha-3b-16bit", "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 "vvsotnikov/stablelm-tuned-alpha-3b-16bit" \ --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": "vvsotnikov/stablelm-tuned-alpha-3b-16bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use vvsotnikov/stablelm-tuned-alpha-3b-16bit with Docker Model Runner:
docker model run hf.co/vvsotnikov/stablelm-tuned-alpha-3b-16bit
Download pytorch_model.bin from vvsotnikov/stablelm-tuned-alpha-3b-16bit: direct link, hf CLI and curl.
- Browser
- Download file 7.54 GB
-
https://huggingface.co/vvsotnikov/stablelm-tuned-alpha-3b-16bit/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://vvsotnikov/stablelm-tuned-alpha-3b-16bit/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vvsotnikov/stablelm-tuned-alpha-3b-16bit/resolve/main/pytorch_model.bin
7.54 GB
- Xet hash:
- 4c2a4716efea9b0546d72e726c018fc08c4ba10208354489da8c3330acbb3a1e
- Size of remote file:
- 7.54 GB
- SHA256:
- 61d771cb399d04e2cdbd436235dec9b780475f91f6c0b936c84889563d12639b
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