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iulio
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Model Details
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iulio
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google/gemma-4-26B-A4B-it
Input Modalities
Output Modalities
Supported Functionality
| Attribute | Specification |
|---|---|
| Base Model | google/gemma-4-26B-A4B-it (Google DeepMind) |
| Architecture | Sparse Mixture-of-Experts (gemma4_unified) with 128 experts, top-8 routing |
| Active Parameters | ~4 Billion active parameters per token (25.82B total parameters) |
| Hardware Used | NVIDIA A100-SXM4-80GB (Modal.com Serverless Cluster) |
| Quantization at Training | 4-bit NF4 with native bfloat16 compute (BitsAndBytesConfig) |
| LoRA Config | r=16, α=32, targeting language model linear projections (q, k, v, o, gate, up, down) |
| Training Steps | 100 steps (effective batch size 8) over 1,719 statutory curriculum items |
| Training Duration | 979.9 seconds (16.33 min) |
| Final Loss | 0.6011 (Mean token accuracy >99.9%) |
| Adapter Size | 35.51 MB SafeTensors |
python
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModel BASE_MODEL = "google/gemma-4-26B-A4B-it"ADAPTER_REPO = "iulio/FiscMind-Gemma4-26B-MoE" tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)base_model = AutoModelForCausalLM.from_pretrained( BASE_MODEL, device_map="auto", dtype=torch.bfloat16,) model = PeftModel.from_pretrained(base_model, ADAPTER_REPO)model.eval() sys_msg = ( "Esti FiscMind AI, expert contabil CECCAR si consultant fiscal CCF conform " "legislatiei din Romania actualizate 2025/2026. Raspunde precis cu temei legal si formule contabile complete.")prompt = f"""<start_of_turn>user{sys_msg} Care este monografia contabila pentru inregistrarea dividendelor interimare trimestriale si ce impozit se retine?<end_of_turn><start_of_turn>model""" inputs = tokenizer(prompt, return_tensors="pt").to(model.device)with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=512, temperature=0.2, do_sample=True, ) print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Developed by Iulian Amaricai as part of the FiscMind AI Romanian Statutory Accounting Assistant initiative. Base model courtesy of Google DeepMind (Gemma 4 Family).
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModel BASE_MODEL = "google/gemma-4-26B-A4B-it"ADAPTER_REPO = "iulio/FiscMind-Gemma4-26B-MoE" tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)base_model = AutoModelForCausalLM.from_pretrained( BASE_MODEL, device_map="auto", dtype=torch.bfloat16,) model = PeftModel.from_pretrained(base_model, ADAPTER_REPO)model.eval() sys_msg = ( "Esti FiscMind AI, expert contabil CECCAR si consultant fiscal CCF conform " "legislatiei din Romania actualizate 2025/2026. Raspunde precis cu temei legal si formule contabile complete.")prompt = f"""<start_of_turn>user{sys_msg} Care este monografia contabila pentru inregistrarea dividendelor interimare trimestriale si ce impozit se retine?<end_of_turn><start_of_turn>model""" inputs = tokenizer(prompt, return_tensors="pt").to(model.device)with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=512, temperature=0.2, do_sample=True, ) print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))