--- license: mit base_model: hpcai-tech/openmoe-base tags: - finance - mixture-of-experts - openmoe - umt5 language: - en --- # Meridian.AI Meridian.AI is an experimental finance-focused sparse Mixture-of-Experts (MoE) causal language model trained via continual updates. ## Intended use Use this model for: - Financial Q&A style prompting - Basic quantitative finance explanations - Summarization/classification-style finance text tasks ## Base model + tokenizer - **Base model weights**: `hpcai-tech/openmoe-base` - **Tokenizer**: `google/umt5-small` (256k vocab, SentencePiece/umT5) This repo includes a working umT5 tokenizer at the root so `AutoTokenizer.from_pretrained("MeridianAlgo/MeridianAI")` works. ## How to use (Transformers) The model weights are stored under the `checkpoint/` subfolder. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer repo_id = "MeridianAlgo/MeridianAI" tokenizer = AutoTokenizer.from_pretrained(repo_id) model = AutoModelForCausalLM.from_pretrained( repo_id, subfolder="checkpoint", trust_remote_code=True, torch_dtype=torch.float32, low_cpu_mem_usage=True, ignore_mismatched_sizes=True, ) model.eval() prompt = """### Instruction: Explain what a P/E ratio is and how it is used. ### Response: """ inputs = tokenizer(prompt, return_tensors="pt") out = model.generate( **inputs, max_new_tokens=128, do_sample=True, temperature=0.85, top_p=0.92, repetition_penalty=1.25, no_repeat_ngram_size=3, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, ) print(tokenizer.decode(out[0], skip_special_tokens=True)) ``` ## Training data Training uses streaming mixes of finance datasets (FinanceMTEB family) plus optional larger corpora depending on environment configuration. ## Limitations - This is a continually trained experimental model and may exhibit repetition. - Not financial advice. - Outputs may be incorrect or outdated. ## Source code Training pipeline and scripts live in the GitHub repo: https://github.com/MeridianAlgo/FinAI