from transformers import pipeline
import torch

print("Loading Supra Mini v6 1M model from Hugging Face...")
pipe = pipeline(
    "text-generation", 
    model="SupraLabs/Supra-Mini-v6-1M",
    device_map="auto",
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
)

def generate_text(prompt, max_length=150):
    result = pipe(
        prompt, 
        max_new_tokens=max_length,
        do_sample=True,
        temperature=0.5,
        top_k=25,
        top_p=0.9,
        repetition_penalty=1.2,
        pad_token_id=pipe.tokenizer.pad_token_id,
        eos_token_id=pipe.tokenizer.eos_token_id
    )
    return result[0]['generated_text']

test_prompt = "The importance of education is"
print(f"\nPrompt: {test_prompt}")
print("-" * 30)
print("\nOutput:\n" + generate_text(test_prompt))

Use cases

Educational research
deployment or testing/fine-tuning on edge environments
Or more simply, for fun

Limitations

Cannot reason, chat, or code
Incoherent more often than not
Mostly unfactual

Training guide

We trained Supra Mini v6 1M on a single NVIDIA RTX 5060 Ti 16GB in ~3 hours for 1 epoch.
The full training code can be found in this repo as train_tokenizer.py (train costum BPE tokenizer with vocab size of 16384) and train_model.py (train the model).
The model was trained on the first 5 billion tokens of 70% Sample-10BT from Fineweb-Edu and 30% Cosmopedia-v2.