Text Generation
Transformers
Safetensors
English
llama
supra
chimera
50m
small
open
open-source
cpu
tiny
slm
text-generation-inference
Instructions to use SupraLabs/Supra-50M-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SupraLabs/Supra-50M-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SupraLabs/Supra-50M-Base")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SupraLabs/Supra-50M-Base") model = AutoModelForCausalLM.from_pretrained("SupraLabs/Supra-50M-Base") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use SupraLabs/Supra-50M-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SupraLabs/Supra-50M-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SupraLabs/Supra-50M-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SupraLabs/Supra-50M-Base
- SGLang
How to use SupraLabs/Supra-50M-Base 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 "SupraLabs/Supra-50M-Base" \ --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": "SupraLabs/Supra-50M-Base", "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 "SupraLabs/Supra-50M-Base" \ --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": "SupraLabs/Supra-50M-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SupraLabs/Supra-50M-Base with Docker Model Runner:
docker model run hf.co/SupraLabs/Supra-50M-Base
| print("[*] Loading libraries...") | |
| import torch | |
| from transformers import LlamaForCausalLM, PreTrainedTokenizerFast | |
| model_path = "./Chimera-FINAL" | |
| print("[*] Loading tokenizer...") | |
| tokenizer = PreTrainedTokenizerFast.from_pretrained(model_path) | |
| print("[*] Loading model...") | |
| model = LlamaForCausalLM.from_pretrained(model_path) | |
| model.eval() | |
| prompt = "Artificial intelligence is " # "Artificial intelligence is " | "The main concept of physics is " | "Once upon a time, " | |
| print(f"[*] Prompt: {prompt!r}") | |
| inputs = tokenizer(prompt, return_tensors="pt") | |
| with torch.no_grad(): | |
| outputs = model.generate( | |
| input_ids=inputs["input_ids"], | |
| attention_mask=inputs["attention_mask"], | |
| max_new_tokens=256, | |
| do_sample=True, | |
| temperature=0.4, | |
| top_p=0.85, | |
| top_k=30, | |
| repetition_penalty=1.1, | |
| pad_token_id=tokenizer.pad_token_id, | |
| eos_token_id=tokenizer.eos_token_id, | |
| ) | |
| print("[*] Output of Supra 50M Base:", tokenizer.decode(outputs[0], skip_special_tokens=True)) | |