from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Mila-AI/-v1.3.7b--aDDont-" # hypothetical path tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
| Benchmark | Expected Score (1.3B) | Mila AI -v1.3.7b- -aDDont- (speculative) | |-----------|----------------------|-------------------------------------------| | HellaSwag (0-shot) | ~45% | ~48% (if well-tuned) | | MMLU (5-shot) | ~25% | ~27% | | HumanEval (pass@1) | ~4% | ~5.5% | | French GLUE (FLeX) | N/A | Could excel (bilingual) | Mila AI -v1.3.7b- -aDDont-
| Component | Candidate Setting | |---------------------|---------------------------------------------| | Layers | 24–28 | | Hidden size | 2048–2560 | | Attention heads | 16–20 | | Context length | 2048 or 4096 tokens | | Activation function | SwiGLU / GELU | | Positional encoding | RoPE or ALiBi | | Training tokens | 300B – 1T (if scaled for 1.3B) | from transformers import AutoModelForCausalLM