KrynexAI 1.4
KrynexAI 1.4 is a compact, ultra-lightweight 150M-parameter language model designed for high-efficiency text generation, fast inference, and on-device deployment.
🧠 Model Summary
KrynexAI 1.4 features an optimized Llama-based architecture trained on a multi-trillion token mix of web content, code, and educational data. It provides strong base capabilities in reasoning, instruction following, and general text processing while maintaining a minimal memory footprint (~270 MB weights).
- Architecture: Llama-based Transformer Decoder
- Parameters: ~150M
- Context Window: 8,192 tokens
- Precision:
bfloat16/fp16
🚀 Quickstart & Usage
pip install transformers
Basic Inference (Python)
import torch from transformers import AutoTokenizer, AutoModelForCausalLM checkpoint = "your-username/KrynexAI-1.4" device = "cuda" if torch.cuda.is_available() else "cpu" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained( checkpoint, torch_dtype=torch.bfloat16, device_map="auto" ) inputs = tokenizer.encode("Gravity is", return_tensors="pt").to(device) outputs = model.generate(inputs, max_new_tokens=50) print(tokenizer.decode(outputs[0], skip_special_tokens=True))
📌 Performance Benchmarks
| Metric | KrynexAI 1.4 |
|---|---|
| HellaSwag | 42.1 |
| ARC (Average) | 43.9 |
| PIQA | 68.4 |
| MMLU (cloze) | 31.5 |
| CommonsenseQA | 33.9 |
| OpenBookQA | 34.6 |
Original author: SmolLM (135M), It's fine-tuned version
📜 License
Apache 2.0