adaption_personal_finance_qa

Model Training

A LORA adapter for meta-llama/Llama-4-Scout-17B-16E-Instruct. This model was trained with SFT using Adaption's AutoScientist on the personal_finance_qa dataset.

AutoScientist Config

{
  "job_id": "c6d2eeca-107f-43dc-bb95-b3f66fad1111",
  "training_experiment_id": "2be2f585-3bd2-4f32-8776-5bd816726930",
  "original_model_name": "meta-llama/Llama-4-Scout-17B-16E-Instruct",
  "trained_model_name": "adaption_personal_finance_qa",
  "training_method": "sft",
  "training_type": "lora",
  "data_format": "chat",
  "hyperparams": {
    "lora": "true",
    "lora_r": 64,
    "n_evals": 5,
    "n_epochs": 4,
    "batch_size": "max",
    "lora_alpha": 128,
    "lora_dropout": 0,
    "min_lr_ratio": 0.1,
    "warmup_ratio": 0.05,
    "weight_decay": 0.03,
    "learning_rate": 0.0001,
    "max_grad_norm": 0.5,
    "base_model_size": "109B",
    "train_on_inputs": "false",
    "training_method": "sft",
    "lr_scheduler_type": "cosine",
    "scheduler_num_cycles": 0.5,
    "lora_trainable_modules": "k_proj,o_proj,q_proj,v_proj,shared_expert.gate_proj,shared_expert.up_proj,shared_expert.down_proj,feed_forward.gate_proj,feed_forward.up_proj,feed_forward.down_proj"
  }
}

Training Data

The model was trained on 35,700 rows of adapted data with the following domain distribution: personal-finance (64%), market-analysis (11%), corporate-business (10%), legal (8%), transportation (1%), language (1%), governance (1%), math (1%), technology (0%), career-workplace (0%), science (0%), history (0%), product-advice (0%), academic-education (0%), data-analysis-visualization (0%), travel (0%), how-to (0%), hr (0%), medical (0%), geography (0%), other (0%), code (0%), news (0%), marketing (0%), personal-growth (0%), sports (0%), cooking (0%), finance (0%), culture (0%), animal-nature (0%), religion (0%), real-estate (0%), tax (0%), games (0%), economics (0%), entertainment (0%), parenting-family (0%).

Model Evaluation

The model was evaluated on an in-distribution held-out test set as well as a broader domain-specific test set to measure generalization.

Domain	Win rate vs. base model
personal-finance	66%

How to use

pip install torch transformers peft

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

BASE = "meta-llama/Llama-4-Scout-17B-16E-Instruct"
ADAPTER = "<this-repo-id>"

device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float32 if device == "cpu" else torch.bfloat16

base = AutoModelForCausalLM.from_pretrained(BASE, dtype=dtype).to(device)
model = PeftModel.from_pretrained(base, ADAPTER)
# Optional: merge the LoRA weights into the base for faster inference
model = model.merge_and_unload()
model.eval()

tokenizer = AutoTokenizer.from_pretrained(BASE)
messages = [{"role": "user", "content": "Hello!"}]
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(device)

with torch.inference_mode():
    out = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))