metadata
base_model: meta-llama/Llama-3.2-3B-Instruct
library_name: peft
license: other
tags:
- lora
- peft
- adapter
- adaption
adaption_math_word_problem_sub_2
Model Training
A LORA adapter for meta-llama/Llama-3.2-3B-Instruct. This model was trained with SFT using Adaption's AutoScientist on the math_word_problem_sub_2 dataset.
AutoScientist Config
{
"job_id": "dfdd2990-1ef7-41e4-829d-2924d668ab65",
"training_experiment_id": "19aa2607-d5ad-43b7-90b2-5e7d3745b627",
"original_model_name": "meta-llama/Llama-3.2-3B-Instruct",
"trained_model_name": "adaption_math_word_problem_sub_2",
"training_method": "sft",
"training_type": "lora",
"data_format": "chat",
"hyperparams": {
"lora": "true",
"lora_r": 16,
"n_evals": 5,
"n_epochs": 3,
"batch_size": "max",
"lora_alpha": 32,
"lora_dropout": 0,
"min_lr_ratio": 0.1,
"warmup_ratio": 0.1,
"weight_decay": 0,
"learning_rate": 0.00001,
"max_grad_norm": 2,
"base_model_size": "3B",
"train_on_inputs": "false",
"training_method": "sft",
"lr_scheduler_type": "cosine",
"scheduler_num_cycles": 0.5,
"lora_trainable_modules": "all-linear"
}
}
Training Data
The model was trained on 19,573 rows of adapted data with the following domain distribution: math (99%), language (0%), science (0%), personal-finance (0%), fitness-sports (0%), animal-nature (0%), agriculture (0%), how-to (0%), sports (0%), travel (0%), data-analysis-visualization (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 |
|---|---|
| math | 50% |
How to use
pip install torch transformers peft
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
BASE = "meta-llama/Llama-3.2-3B-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))