adaption_commonsenseqa_augmented

Model Training

A LORA adapter for Qwen/Qwen3.5-0.8B. This model was trained with SFT using Adaption's AutoScientist on the commonsenseqa (augmented) dataset.

AutoScientist Config

{
  "job_id": "b0b7254e-1f27-4ea8-aefa-0dc4be596307",
  "training_experiment_id": "107060cc-e553-46c6-9919-b7c61200c828",
  "original_model_name": "Qwen/Qwen3.5-0.8B",
  "trained_model_name": "adaption_commonsenseqa_augmented",
  "training_method": "sft",
  "training_type": "lora",
  "data_format": "chat",
  "hyperparams": {
    "lora": "true",
    "lora_r": 64,
    "n_evals": 5,
    "n_epochs": 5,
    "batch_size": "max",
    "lora_alpha": 128,
    "lora_dropout": 0.1,
    "min_lr_ratio": 0.1,
    "warmup_ratio": 0.05,
    "weight_decay": 0.01,
    "learning_rate": 0.0003,
    "max_grad_norm": 1,
    "base_model_size": "0.8B",
    "train_on_inputs": "false",
    "training_method": "sft",
    "lr_scheduler_type": "cosine",
    "scheduler_num_cycles": 0.5,
    "lora_trainable_modules": "k_proj,up_proj,o_proj,q_proj,down_proj,v_proj,gate_proj"
  }
}

Training Data

The model was trained on 42,593 rows of adapted data with the following domain distribution: market-analysis (45%), other (33%), personal-finance (3%), news (2%), science (2%), governance (2%), entertainment (2%), architecture-design (2%), code (1%), legal (1%), sports (1%), medical (1%), language (1%), fashion-beauty (1%), art (1%), games (1%), how-to (1%), travel (1%), agriculture (1%), parenting-family (1%), corporate-business (1%), writing-editing-communication (1%).

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
general	61%

How to use

pip install torch transformers peft

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

BASE = "Qwen/Qwen3.5-0.8B"
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))