Text Generation
Transformers
Safetensors
English
gpts2
language-model
transformer
rope
swiglu
gqa
xsa
custom-architecture
custom-tokenizer
custom_code
Instructions to use AxiomicLabs/GPT-S-5M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AxiomicLabs/GPT-S-5M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AxiomicLabs/GPT-S-5M", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("AxiomicLabs/GPT-S-5M", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AxiomicLabs/GPT-S-5M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AxiomicLabs/GPT-S-5M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AxiomicLabs/GPT-S-5M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AxiomicLabs/GPT-S-5M
- SGLang
How to use AxiomicLabs/GPT-S-5M 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 "AxiomicLabs/GPT-S-5M" \ --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": "AxiomicLabs/GPT-S-5M", "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 "AxiomicLabs/GPT-S-5M" \ --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": "AxiomicLabs/GPT-S-5M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AxiomicLabs/GPT-S-5M with Docker Model Runner:
docker model run hf.co/AxiomicLabs/GPT-S-5M
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| model_name = "AxiomicLabs/GPT-S-5M" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_name, | |
| trust_remote_code=True, | |
| torch_dtype=torch.bfloat16, | |
| device_map="auto", | |
| ) | |
| prompt = "The future of AI is" | |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) | |
| with torch.inference_mode(): | |
| output = model.generate( | |
| **inputs, | |
| max_new_tokens=120, | |
| do_sample=True, | |
| temperature=0.8, | |
| top_p=0.95, | |
| repetition_penalty=1.1, | |
| no_repeat_ngram_size=4, | |
| ) | |
| text = tokenizer.decode(output[0], skip_special_tokens=True) | |
| print(text) | |