diff --git a/.gitattributes b/.gitattributes
index 381b2d4f4f0275f80fbbf3aa0711ca295a5dea6f..b1b537cd2b3c6037bcc3a743436fe95d5fadbca5 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -91,3 +91,5 @@ data/IPTS-30384/REFL_206968_combined_data_auto.txt filter=lfs diff=lfs merge=lfs
 data/IPTS-30384/REFL_207087_combined_data_auto.txt filter=lfs diff=lfs merge=lfs -text
 data/IPTS-30384/REFL_207304_combined_data_auto.txt filter=lfs diff=lfs merge=lfs -text
 data/IPTS-30384/REFL_207358_combined_data_auto.txt filter=lfs diff=lfs merge=lfs -text
+models/model_gpt_d2048_h32_l6.pt filter=lfs diff=lfs merge=lfs -text
+data/IPTS-29196 filter=lfs diff=lfs merge=lfs -text
diff --git a/models/model_gpt_d2048_h32_l6.pt b/models/model_gpt_d2048_h32_l6.pt
new file mode 100644
index 0000000000000000000000000000000000000000..207436fe1928ec75d4224fce71f79cb1cba4eea2
--- /dev/null
+++ b/models/model_gpt_d2048_h32_l6.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8cf1cfe569126426ea643696668d6f9fcf5379aa48413b6bd7cdf001249ee6b5
+size 646837393
diff --git a/notebooks/model_eval_demo.ipynb b/notebooks/model_eval_demo.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6cdb72e320cac36a7cc60a2af7149d4d69f0f710
--- /dev/null
+++ b/notebooks/model_eval_demo.ipynb
@@ -0,0 +1,170 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Overview\n",
+    "\n",
+    "Example notebook showcasing how to use pre-defined functions to perform quick evaluation of trained model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from tgreft.models.refl_gpt import REFL_GPT\n",
+    "from tgreft.analysis.evaluate import evaluate\n",
+    "\n",
+    "# utils\n",
+    "from tqdm.auto import tqdm\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# global settings\n",
+    "plt.rcParams['figure.dpi'] = 120"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/8cz/micromamba/envs/tgreft_dev/lib/python3.11/site-packages/torch/nn/modules/transformer.py:282: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance)\n",
+      "  warnings.warn(f\"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}\")\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "REFL_GPT(\n",
+       "  (embedding): Linear(in_features=150, out_features=2048, bias=True)\n",
+       "  (positional_encoding): PositionalEncoding()\n",
+       "  (transformer_encoder): TransformerEncoder(\n",
+       "    (layers): ModuleList(\n",
+       "      (0-5): 6 x TransformerEncoderLayer(\n",
+       "        (self_attn): MultiheadAttention(\n",
+       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=2048, out_features=2048, bias=True)\n",
+       "        )\n",
+       "        (linear1): Linear(in_features=2048, out_features=2048, bias=True)\n",
+       "        (dropout): Dropout(p=0.1, inplace=False)\n",
+       "        (linear2): Linear(in_features=2048, out_features=2048, bias=True)\n",
+       "        (norm1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
+       "        (norm2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
+       "        (dropout1): Dropout(p=0.1, inplace=False)\n",
+       "        (dropout2): Dropout(p=0.1, inplace=False)\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (decoder): Linear(in_features=2048, out_features=17, bias=True)\n",
+       ")"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# weights location\n",
+    "model_file = \"../models/model_gpt_d2048_h32_l6.pt\"\n",
+    "\n",
+    "# define model\n",
+    "model = REFL_GPT(\n",
+    "    d_model=2048,\n",
+    "    nhead=16,\n",
+    "    num_encoder_layers=6,\n",
+    "    input_dim=150,\n",
+    "    output_dim=17,\n",
+    "    to_log=True,\n",
+    ")\n",
+    "\n",
+    "# load weights\n",
+    "model.load_state_dict(\n",
+    "    torch.load(\n",
+    "        model_file,\n",
+    "        map_location=torch.device(\"cpu\"),  # CPU is sufficient for inference\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# set to evaluation model\n",
+    "# - turn off grad tracking\n",
+    "# - turn off dropout\n",
+    "model.eval()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1440x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "csv_file = \"../data/IPTS-29196/REFL_201095_combined_data_auto.txt\"\n",
+    "evaluate(\n",
+    "    csv_file,\n",
+    "    model,\n",
+    "    save=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/tgreft/analysis/__init__.py b/src/tgreft/analysis/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/tgreft/analysis/evaluate.py b/src/tgreft/analysis/evaluate.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5e567c49576f567515389bd6c6b603925a88cfc
--- /dev/null
+++ b/src/tgreft/analysis/evaluate.py
@@ -0,0 +1,275 @@
+"""Functions used to evaluate the performance of the model."""
+#!/usr/bin/env python
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+from typing import Tuple, Optional
+from refl1d.names import Experiment, QProbe, Parameter
+from refl1d.names import FitProblem
+from bumps.fitters import fit
+from tgreft.utils.data.data_synthesis import RCurveGenerator
+from tgreft.utils.data.data_loader import param_to_rcurve
+from tgreft.analysis.utils import interpolate_data, load_csv
+
+
+def parameters_refine(
+    param: np.ndarray,
+    r_curve: np.ndarray,
+    q_range: Optional[np.ndarray] = None,
+    dq: Optional[np.ndarray] = None,
+    error: Optional[np.ndarray] = None,
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """Refine the parameters with fitting.
+
+    Parameters
+    ----------
+    param : np.ndarray
+        The parameters to refine.
+        0: electrolyte_sld
+        1: electrolyte_roughness
+        2: sei_sld
+        3: sei_thickness
+        4: sei_roughness
+        5: material_sld
+        6: material_thickness
+        7: material_roughness
+        8: cu_sld
+        9: cu_thickness
+        10: cu_roughness
+        11: ti_sld
+        12: ti_thickness
+        13: ti_roughness
+        14: oxide_sld
+        15: oxide_thickness
+        16: oxide_roughness
+    r_curve : np.ndarray
+        The r_curve to fit.
+    error : Optional[np.ndarray], optional
+        The error of the r_curve, by default None.
+
+    Returns
+    -------
+    np.ndarray
+        The refined parameters.
+    np.ndarray
+        The z data from guess sample.
+    np.ndarray
+        The sld data from guess sample.
+    np.ndarray
+        The z data from fitted sample.
+    np.ndarray
+        The sld data from fitted sample.
+    """
+    # create a fresh generator
+    r_generator = RCurveGenerator()
+
+    # build the sample from the parameters
+    config = [
+        {"name": "electrolyte", "sld": param[0], "isld": 0, "thickness": 0, "roughness": param[1]},
+        {"name": "SEI", "sld": param[2], "isld": 0, "thickness": param[3], "roughness": param[4]},
+        {"name": "material", "sld": param[5], "isld": 0, "thickness": param[6], "roughness": param[7]},
+        {"name": "Cu", "sld": param[8], "isld": 0, "thickness": param[9], "roughness": param[10]},
+        {"name": "Ti", "sld": param[11], "isld": 0, "thickness": param[12], "roughness": param[13]},
+        {"name": "oxide", "sld": param[14], "isld": 0, "thickness": param[15], "roughness": param[16]},
+        {"name": "substrate", "sld": 2.07, "isld": 0, "thickness": 0, "roughness": 0},
+    ]
+    sample = r_generator.build_sample_from_config(config)
+
+    # set parameter refine ranges
+    sample["electrolyte"].material.rho.range(0, 7)
+    sample["electrolyte"].interface.range(5, 300)
+
+    sample["SEI"].material.rho.range(-3, 10)
+    sample["SEI"].interface.range(5, 100)
+    sample["SEI"].thickness.range(10, 1000)
+
+    sample["material"].material.rho.range(-3, 10)
+    sample["material"].interface.range(5, 50)
+    sample["material"].thickness.range(10, 1000)
+
+    sample["Cu"].material.rho.range(-3, 10)
+    sample["Cu"].interface.range(0, 50)
+    sample["Cu"].thickness.range(10, 1000)
+
+    sample["Ti"].material.rho.range(-3, 3)
+    sample["Ti"].interface.range(0, 50)
+    sample["Ti"].thickness.range(10, 1000)
+
+    sample["oxide"].material.rho.range(0, 5)
+    sample["oxide"].interface.range(0, 25)
+    sample["oxide"].thickness.range(0, 60)
+
+    data = r_curve
+
+    # make a probe
+    if q_range is None:
+        q_range = np.logspace(
+            np.log10(0.009),
+            np.log10(0.18),
+            num=150,
+        )
+
+    if dq is None:
+        q_resolution = 0.025
+        dq = q_resolution * q_range / 2.355
+
+    if error is None:
+        error = r_curve * 0.07  # 7% error, matching the generated synthetic data
+
+    probe = QProbe(q_range, dq, data=(data, error))
+    probe.background = Parameter(value=0.0, name="background")
+
+    # make an experiment
+    experiment = Experiment(probe=probe, sample=sample)
+
+    # get the sld profile from guessed sample
+    z_guessed, sld_guessed, _ = experiment.smooth_profile()
+
+    # refine
+    problem = FitProblem(experiment)
+
+    # Try this first (faster)
+    try:
+        results = fit(problem, method="amoeba", steps=10000, verbose=False)
+    except Exception as error:
+        print(error)
+        print("!!Switch to dream!!")
+        print(f"--> guess: {param}")
+        results = fit(problem, method="dream", steps=1000, burn=1000, pop=20, verbose=False)
+        # re-throw the error so that the caller can handle it
+        raise Exception(error)
+
+    # get the sld profile along z
+    z_fitted, sld_fitted, _ = experiment.smooth_profile()
+
+    # Results are in the wrong order. It's interface, rho, thickness...
+    fit_pars = [results.x[1], results.x[0]]
+    n_layers = int((len(results.x) - 1) / 3)
+    for i in range(n_layers):
+        fit_pars.extend([results.x[3 * i + 3], results.x[3 * i + 4], results.x[3 * i + 2]])
+
+    return np.array(fit_pars), z_guessed, sld_guessed, z_fitted, sld_fitted
+
+
+def extract_parameters(
+    csv_file: str,
+    model: torch.nn.Module,
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """Extract the parameters from reflectometry data (CSV) via inference and refinement.
+
+    Parameters
+    ----------
+    csv_file : str
+        The path to the CSV file containing the data.
+    model : torch.nn.Module
+        The model to use for inference.
+
+    Returns
+    -------
+    param : np.ndarray
+        The refined parameters.
+    guess : np.ndarray
+        The parameters from inference.
+    z_guessed : np.ndarray
+        The z data from guess sample.
+    sld_guessed : np.ndarray
+        The sld data from guess sample.
+    z_fitted : np.ndarray
+        The z data from fitted sample.
+    sld_fitted : np.ndarray
+        The sld data from fitted sample.
+    """
+    # load the data from the CSV file
+    q, r, dr, dq = load_csv(csv_file)
+    # interpolate the data to the native q_range
+    q_range, r_interpolated, dr_interpolated = interpolate_data(q, r, dr)
+    # convert to torch tensors
+    r_interpolated = torch.tensor(r_interpolated, dtype=torch.float32)
+    # infer the parameters
+    with torch.no_grad():
+        # replace non-positive values with 1 so that they become 0 after log
+        r_interpolated[r_interpolated <= 0] = 1
+        guess = model(r_interpolated.unsqueeze(0)).squeeze(0).numpy()
+    # if nan in the guess, print the filename
+    if np.isnan(guess).any():
+        param = None
+        z_guessed, sld_guessed, z_fitted, sld_fitted = None, None, None, None
+    else:
+        # refine the parameters
+        # NOTE: use the data from the CSV file for refinement
+        param, z_guessed, sld_guessed, z_fitted, sld_fitted = parameters_refine(
+            param=guess,
+            r_curve=r,
+            q_range=q,
+            dq=dq,
+            error=dr,
+        )
+    return param, guess, z_guessed, sld_guessed, z_fitted, sld_fitted
+
+
+def evaluate(
+    csv_file: str,
+    model: torch.nn.Module,
+    save: bool = False,
+):
+    """Evaluate the model on the data from the CSV file.
+
+    Parameters
+    ----------
+    csv_file : str
+        The path to the CSV file containing the data.
+    model : torch.nn.Module
+        The model to use for inference.
+    save : bool, optional
+        Whether to save the plot, by default False.
+    """
+    # extract the parameters
+    param, guess, z_guessed, sld_guessed, z_fitted, sld_fitted = extract_parameters(csv_file, model)
+
+    # error handling
+    # NOTE: we need to figure out why we are getting NaN here
+    if param is None or np.isnan(param).any():
+        print(f"For {csv_file}:\nparam: {param}\nguess: {guess}\n")
+        return
+
+    # load the data from the CSV file
+    q, r, dr, dq = load_csv(csv_file)
+    # interpolate the data to the native q_range (just need the q_range here)
+    q_range, r_interpolated, dr_interpolated = interpolate_data(q, r, dr)
+    # get the r_curve from the parameters
+    r_guess = param_to_rcurve(guess)
+    r_refined = param_to_rcurve(param)
+
+    # plot
+    fig, axs = plt.subplots(1, 2, figsize=(12, 4))
+    ax1, ax2 = axs
+    # plt keywords
+    data_kwargs = {"label": "data", "color": "blue", "fmt": "+", "alpha": 0.5}
+    guess_kwargs = {"label": "guess", "color": "orange"}
+    refined_kwargs = {"label": "refined", "color": "green"}
+    # ax1
+    ax1.errorbar(q, r, yerr=dr, **data_kwargs)
+    ax1.plot(q_range, r_guess, **guess_kwargs)
+    ax1.plot(q_range, r_refined, **refined_kwargs)
+    ax1.set_yscale("log")
+    ax1.set_xlabel("q (1/A)")
+    ax1.set_ylabel("r")
+    ax1.legend()
+    # ax2
+    ax2.plot(z_guessed, sld_guessed, **guess_kwargs)
+    ax2.plot(z_fitted, sld_fitted, **refined_kwargs)
+    ax2.set_xlabel("z")
+    ax2.set_ylabel("sld")
+    ax2.legend()
+    # title
+    fig.suptitle(csv_file)
+
+    if save:
+        plt.savefig(csv_file.replace(".txt", ".pdf"), dpi=120)
+        plt.close()
+    else:
+        plt.show()
+
+
+if __name__ == "__main__":
+    pass
diff --git a/src/tgreft/analysis/utils.py b/src/tgreft/analysis/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..698ef65f46fc656cf0eb97205c12b601185a7baa
--- /dev/null
+++ b/src/tgreft/analysis/utils.py
@@ -0,0 +1,95 @@
+"""Utility functions useful for evaluting the performance of a model."""
+#!/usr/bin/env python
+import numpy as np
+from typing import Tuple
+
+
+def load_csv(csv_file: str) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Load the data from the CSV file and return the q, r, and dr data as numpy arrays.
+
+    Parameters
+    ----------
+    csv_file : str
+        The path to the CSV file containing the data.
+
+    Returns
+    -------
+    q : np.ndarray
+        The q data.
+    r : np.ndarray
+        The r data.
+    dr : np.ndarray
+        The dr data.
+
+    Examples
+    --------
+    >>> q, r, dr = load_csv("data/IPTS-29196/REFL_201083_combined_data_auto.txt")
+    """
+    # load the data from the CSV file
+    # NOTE: default arg is sufficient for the data format
+    data = np.loadtxt(csv_file)
+    # separate into columns
+    data_q = data[:, 0]  # 1/A
+    data_r = data[:, 1]  #
+    data_dr = data[:, 2]  #
+    data_dq = data[:, 3]  # 1/A
+    return data_q, data_r, data_dr, data_dq
+
+
+def interpolate_data(q: np.ndarray, r: np.ndarray, dr: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Interpolate the data to the native q_range.
+
+    Parameters
+    ----------
+    q : np.ndarray
+        The q data.
+    r : np.ndarray
+        The r data.
+    dr : np.ndarray
+        The dr data.
+
+    Returns
+    -------
+    q : np.ndarray
+        The q data, interpolated to the native q_range.
+    r : np.ndarray
+        The r data, interpolated to the native q_range.
+    dr : np.ndarray
+        The dr data, interpolated to the native q_range.
+    """
+    # native q_range
+    q_range = np.logspace(
+        np.log10(0.009),
+        np.log10(0.18),
+        num=150,
+    )
+    # interpolate the data to the native q_range
+    r = np.interp(q_range, q, r)
+    dr = np.interp(q_range, q, dr)
+    return q_range, r, dr
+
+
+def get_rcurve_from_csv(csv_file: str) -> Tuple[np.ndarray, np.ndarray]:
+    """Load the data from the CSV file and return the r_curve and dq data as numpy arrays.
+
+    Parameters
+    ----------
+    csv_file : str
+        The path to the CSV file containing the data.
+
+    Returns
+    -------
+    r_curve : np.ndarray
+        The r_curve data, interpolated to the same q range as the simulated data.
+    dq : np.ndarray
+        The dq data, interpolated to the same q range as the simulated data.
+    """
+    # load the data from the CSV file
+    q, r, dr, _ = load_csv(csv_file)
+    # interpolate the data to the native q_range
+    _, r, dr = interpolate_data(q, r, dr)
+    return r, dr
+
+
+if __name__ == "__main__":
+    pass