diff --git a/docs/file_formats.md b/docs/file_formats.md
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+# File formats
+
+## Input file formats for Casanovo
+
+When you're ready to use Casanovo for *de novo* peptide sequencing, you can input your MS/MS spectra in one of the following formats:
+
+- **[mzML](https://doi.org/10.1074/mcp.R110.000133)**: XML-based mass spectrometry community standard file format developed by the Proteomics Standards Initiative (PSI).
+- **[mzXML](https://doi.org/10.1038/nbt1031)**: XML-based predecessor of mzML. Although supported by Casanovo, mzML should typically be preferred instead.
+- **[MGF](https://www.matrixscience.com/help/data_file_help.html)**: A simple text-based peak file, though not as rich in detail as mzML.
+
+All three of the above file formats can be used as input to Casanovo for *de novo* peptide sequencing.
+As the official PSI standard format containing the complete information from a mass spectrometry run, mzML should typically be preferred.
+
+## Output: Understanding the mzTab format
+
+After Casanovo processes your input file(s), it provides the sequencing results in an **[mzTab]((https://doi.org/10.1074/mcp.O113.036681))** file.
+This file is divided into two main sections:
+
+1. **Metadata section**: This part describes general information about the file and the Casanovo sequencing task.
+2. **Peptide–spectrum match (PSM) section**: Details of the peptide sequences that Casanovo predicted for the MS/MS spectra.
+
+mzTab files can contain additional sections to include protein identifications and quantification information as well.
+However, as these levels of information are not relevant for Casanovo, these are not included in its output mzTab files.
+
+> [!TIP]
+> mzTab is a human and machine readable format.
+> It can be inspected manually by opening it with a text editor or with spreadsheet software (specify tab as the delimiter).
+> Additionally, you can use tools like [Pyteomics](https://pyteomics.readthedocs.io/en/latest/api/mztab.html) for Python or [MSnbase](https://rdrr.io/bioc/MSnbase/man/readMzTabData.html) for R to programmatically read mzTab files.
+
+**Metadata section**
+
+The metadata section consists of three columns, each separated by a tab:
+1. The prefix `MTD` indicating that this is the metadata section.
+2. A key describing a metadata item.
+3. The value corresponding to the metadata key.
+
+As an example, these are the first few lines in an mzTab output file produced by Casanovo:
+
+```
+MTD	mzTab-version	1.0.0
+MTD	mzTab-mode	Summary
+MTD	mzTab-type	Identification
+MTD	description	Casanovo identification file my_example_output
+MTD	software[1]	[MS, MS:1003281, Casanovo, 4.0.1]
+```
+
+This identifies this mzTab file with filename "my_example_output" as a summary-level identification file produced by Casanovo.
+On the final line you can see a typical key–value entry using information defined in the [PSI-MS controlled vocabulary](https://github.com/HUPO-PSI/psi-ms-CV/).
+In this case, the line indicates that the file is produced by the Casanovo software, which is recorded in the `MS` controlled vocabulary with accession number `MS:1003281`.
+The final element is the version number of Casanovo that produced this file.
+
+The next few lines typically list the post-translational modifications (PTMs) that Casanovo knew:
+
+```
+MTD	fixed_mod[1]	[UNIMOD, UNIMOD:4, Carbamidomethyl, ]
+MTD	fixed_mod[1]-site	C
+MTD	variable_mod[1]	[UNIMOD, UNIMOD:7, Deamidated, ]
+MTD	variable_mod[1]-site	N
+MTD	variable_mod[2]	[UNIMOD, UNIMOD:7, Deamidated, ]
+MTD	variable_mod[2]-site	Q
+MTD	variable_mod[3]	[UNIMOD, UNIMOD:35, Oxidation, ]
+MTD	variable_mod[3]-site	M
+MTD	variable_mod[4]	[UNIMOD, UNIMOD:385, Ammonia-loss, ]
+MTD	variable_mod[4]-site	N-term
+MTD	variable_mod[5]	[UNIMOD, UNIMOD:1, Acetyl, ]
+MTD	variable_mod[5]-site	N-term
+MTD	variable_mod[6]	[UNIMOD, UNIMOD:5, Carbamyl, ]
+MTD	variable_mod[6]-site	N-term
+```
+
+This indicates that cysteine carbamidomethylation was used as a static modification (this time defined by the [Unimod](https://www.unimod.org/) controlled vocabulary), and that deamidation of asparagine and glutamine, oxidation of methionine, N-terminal loss of ammonia, N-terminal acetylation, and N-terminal carbamylation were used as variable modifications.
+Different PTMs in Casanovo can only be enabled or disabled by training a new model.
+
+The final piece of information in the metadata section is the active configuration settings, allowing for replication or review of the analysis parameters:
+
+```
+MTD	software[1]-setting[1]	model = casanovo_massivekb_v4_0_0.ckpt
+MTD	software[1]-setting[2]	config_filename = default
+MTD	software[1]-setting[3]	precursor_mass_tol = 50.0
+MTD	software[1]-setting[4]	isotope_error_range = (0, 1)
+MTD	software[1]-setting[5]	min_peptide_len = 6
+MTD	software[1]-setting[6]	predict_batch_size = 1024
+MTD	software[1]-setting[7]	n_beams = 10
+MTD	software[1]-setting[8]	top_match = 1
+MTD	software[1]-setting[9]	accelerator = auto
+MTD	software[1]-setting[10]	devices = None
+MTD	software[1]-setting[11]	random_seed = 454
+MTD	software[1]-setting[12]	n_log = 1
+MTD	software[1]-setting[13]	tb_summarywriter = None
+MTD	software[1]-setting[14]	save_top_k = 5
+MTD	software[1]-setting[15]	model_save_folder_path =
+MTD	software[1]-setting[16]	val_check_interval = 50000
+MTD	software[1]-setting[17]	n_peaks = 150
+MTD	software[1]-setting[18]	min_mz = 50.0
+MTD	software[1]-setting[19]	max_mz = 2500.0
+MTD	software[1]-setting[20]	min_intensity = 0.01
+MTD	software[1]-setting[21]	remove_precursor_tol = 2.0
+MTD	software[1]-setting[22]	max_charge = 10
+MTD	software[1]-setting[23]	dim_model = 512
+MTD	software[1]-setting[24]	n_head = 8
+MTD	software[1]-setting[25]	dim_feedforward = 1024
+MTD	software[1]-setting[26]	n_layers = 9
+MTD	software[1]-setting[27]	dropout = 0.0
+MTD	software[1]-setting[28]	dim_intensity = None
+MTD	software[1]-setting[29]	max_length = 100
+MTD	software[1]-setting[30]	warmup_iters = 100000
+MTD	software[1]-setting[31]	max_iters = 600000
+MTD	software[1]-setting[32]	learning_rate = 0.0005
+MTD	software[1]-setting[33]	weight_decay = 1e-05
+MTD	software[1]-setting[34]	train_label_smoothing = 0.01
+MTD	software[1]-setting[35]	train_batch_size = 32
+MTD	software[1]-setting[36]	max_epochs = 30
+MTD	software[1]-setting[37]	num_sanity_val_steps = 0
+MTD	software[1]-setting[38]	train_from_scratch = True
+MTD	software[1]-setting[39]	calculate_precision = False
+MTD	software[1]-setting[41]	n_workers = 20
+MTD	ms_run[1]-location	file://[...]/my_example_input.mgf
+```
+
+**PSM section**
+
+The PSM section in mzTab files starts with a header line, indicated by the `PSH` key, which defines the subsequent tabular PSM information.
+Next, the following lines each start with the `PSM` key and contain information for an individual PSM predicted by Casanovo.
+
+```
+PSH	sequence	PSM_ID	accession	unique	database	database_version	search_engine	search_engine_score[1]	modifications	retention_time	charge	exp_mass_to_charge	calc_mass_to_charge	spectra_ref	pre	post	start	end	opt_ms_run[1]_aa_scores
+PSM	EPPTPLTYVAGAGSGVR	1	null	null	null	null	[MS, MS:1003281, Casanovo, 4.0.1]	0.968312939008077	null	null	2.0	836.439	836.4386613168799	ms_run[1]:index=0	null	null	null	null	0.96454,0.90841,0.97874,0.97979,0.97915,0.98254,0.98184,0.97898,0.86762,0.97782,0.97771,0.97899,0.97987,0.97788,0.97949,0.98074,0.97561
+PSM	VVHGFYNPAVSRVLEQ	2	null	null	null	null	[MS, MS:1003281, Casanovo, 4.0.1]	0.9652494998539195	null	null	3.0	605.6572	605.65644936688	ms_run[1]:index=1	null	null	null	null	0.96870,0.97701,0.85667,0.97274,0.97827,0.97790,0.97829,0.97706,0.97654,0.97725,0.97778,0.95544,0.95622,0.96240,0.96992,0.96909
+PSM	EPPTPLTYVAGGSLNR	3	null	null	null	null	[MS, MS:1003281, Casanovo, 4.0.1]	0.813004752730622	null	null	2.0	836.4398	836.4386608168799	ms_run[1]:index=2	null	null	null	null	0.78636,0.45168,0.64947,0.68432,0.89344,0.90091,0.90124,0.56938,0.89757,0.90204,0.90129,0.90190,0.80076,0.90097,0.90233,0.87599
+PSM	LERPFVHLM+15.995VFLGGSGR	4	null	null	null	null	[MS, MS:1003281, Casanovo, 4.0.1]	0.758128507890635	null	null	4.0	483.7627	483.51345739187997	ms_run[1]:index=3	null	null	null	null	0.86884,0.85508,0.87392,0.39732,0.87556,0.87291,0.69642,0.87083,0.79858,0.86588,0.86291,0.84178,0.45706,0.52835,0.85704,0.41526,0.83419
+PSM	GEYKLLPFNKLMLGEG	5	null	null	null	null	[MS, MS:1003281, Casanovo, 4.0.1]	-0.18260370983796959	null	null	3.0	602.99817	603.6586910335465	ms_run[1]:index=4	null	null	null	null	0.64453,0.77152,0.90248,0.84100,0.65059,0.89975,0.87886,0.82220,0.90324,0.90056,0.88657,0.86091,0.66978,0.63579,0.81815,0.90577
+...
+```
+
+Key information for each PSM is as follows:
+- `sequence`: The predicted peptide sequence.
+- `PSM_ID`: A monotonically increasing index, serving as a unique identifier for each PSM.
+- `search_engine_score[1]`: The score of this PSM.
+- `spectra_ref`: Unique identifier linking the prediction back to the original spectrum in the input file(s).
+- `opt_ms_run[1]_aa_scores`: Casanovo predicts peptides in an autoregressive fashion, one amino acid at a time. This column contains comma-separated scores of the individual amino acid predictions.
+
+> [!NOTE]
+> Scores in Casanovo range from -1 to 1, where 1 indicates high confidence in the prediction.
+> A score below 0 occurs for a predicted peptide sequence that mismatches the observed precursor mass, in which case the score is penalized by subtracting 1.
+> This will also be evident from a difference in the observed precursor _m_/_z_, in the `exp_mass_to_charge` column, and the precursor _m_/_z_ calculated from the predicted peptide sequence, in the `calc_mass_to_charge` column.
+> Hence, it is important to properly configure settings that impact the precursor mass filter, such as the precursor mass tolerance (option `precursor_mass_tol`) and the isotopes to consider (option `isotope_error_range`).
+
+The `spectra_ref` column is essential for connecting predictions back to the corresponding MS/MS spectra in the input file(s).
+This column consists of two parts: the run index and the spectrum reference, separated by a colon.
+- The run index is of the form `ms_run[FILE_INDEX]`, with `FILE_INDEX` referring to the corresponding run location in the metadata section. In the typical case when only a single input file was processed, this will be `1`.
+- The spectrum reference can take the form of either a scan number or a spectrum index.
+    - When using mzML or mzXML files as input, the spectrum reference will take the form of a scan number, encoded as `scan=SCAN`, with `SCAN` the scan number specified in the input file for this spectrum.
+    - When using MGF files as input, the spectrum reference will be an index, encoded as `index=INDEX`, with `INDEX` the zero-based index of the spectrum in its input file. This is because MGF is not a standardized format that is not guaranteed to contain specific spectrum identifiers.
+
+> [!WARNING]
+> Be mindful of the input peak file format when linking Casanovo PSMs to their input spectra.
+> Even when the same raw file is converted to both mzML and MGF, scan numbers in the mzML file will generally not match spectrum indices in the MGF file, as the former contains both MS and MS/MS spectra while the latter only contains MS/MS spectra.
+
+> [!NOTE]
+> The PSM identifier in the `PSM_ID` column is not necessarily identical to the spectrum index in the `spectra_ref` column, even for MGF files.
+> - `PSM_ID` is one-based, whereas spectrum indices in `spectra_ref` are zero-based.
+> - If multiple predictions are included per spectrum (configuration option `top_match`), each PSM will have a different identifier, but spectrum references will overlap.
+
+## Casanovo configuration
+
+Casanovo operates based on settings defined in a [YAML configuration file](https://github.com/Noble-Lab/casanovo/blob/main/casanovo/config.yaml).
+This file contains several options that affect how Casanovo processes your data and predicts peptide sequences.
+If you run Casanovo without specifying a configuration file, it uses a set of default settings.
+However, you might want to adjust these settings for several reasons, such as to capture specific characteristics of your data or to experiment with different training configurations.
+
+To create a custom configuration file, you can start by generating a copy of the default configuration:
+
+```sh
+casanovo configure
+```
+
+You can then edit this file to adjust various settings.
+After editing, specify your custom configuration file when running Casanovo with the `--config` option.
+
+The configuration file is divided into sections, each containing options that are relevant to different phases of Casanovo's operation.
+The first section contains options used to configure Casanovo during *de novo* peptide sequencing, followed by options in the second section that can only be modified when training a new model.
+For example, the `top_match` option in the first section makes it possible to flexibly report multiple PSMs per spectrum during _de novo_ peptide sequencing.
+In contrast, setting a different value for the `n_peaks` option in the second section is only possible when training a new model, and cannot be modified when predicting with a previously trained model that uses a different configuration.
+
+> [!TIP]
+> Each change in the configuration can lead to different outcomes in the peptide sequencing process, so it may be beneficial to experiment with various settings to find the optimal configuration for your data.
+> Always consider your experimental design and the nature of your data when adjusting these settings.
+
+## Logging
+
+Casanovo generates detailed log files during operation, providing insights into its performance and aiding in troubleshooting.
+These log files are named similarly to the output mzTab files but with a `.log` extension.
+Log files detail every step Casanovo takes, including:
+
+- Starting and ending timestamps of the sequencing or training process.
+- Configuration options used.
+- Warnings or errors encountered during processing, providing clues for troubleshooting.
+- Summary statistics upon completion, offering a quick overview of the results.
+
+> [!TIP]
+> Tips for using log files:
+> - Bug reporting: When encountering issues, including the relevant log file in your bug report can significantly aid in diagnosing the problem.
+> - Performance monitoring: Log files can be used to monitor the efficiency of Casanovo's operation over time, identifying potential bottlenecks.
+
+## For advanced users: Training Casanovo
+
+To train a new Casanovo model, the training and validation data must be provided as **annotated MGF files**.
+Annotated MGF files are similar to standard MGF files but include a `SEQ` key–value pair in the spectrum header, indicating the peptide sequence for the corresponding spectrum.
+
+Example of an annotated MGF file entry:
+
+```
+BEGIN IONS
+TITLE=My spectrum title
+PEPMASS=602.2881
+CHARGE=2+
+RTINSECONDS=985.44604
+SEQ=HQGVM+15.995VGM+15.995GQK
+84.08081817626953 0.1001848503947258
+87.74003601074219 0.07622149586677551
+...
+END IONS
+```
+
+> [!NOTE]
+> In case the peptide sequence includes PTMs, ensure that these are formatted correctly and match the amino acid and modification vocabulary in the Casanovo configuration.
+
+mzML or mzXML files are not supported as input during training, as these formats do not provide a mechanism to annotate their spectra with peptide sequences.
+Similarly, in Casanovo evaluation mode only annotated MGF files are supported.
+
+<!-- TODO: when index files can be reused, document this here -->
+
+During training, Casanovo will save **checkpoint files** at every `val_check_interval` steps, specified in the configuration.
+Model checkpoints will be saved in the `model_save_folder_path` folder with filename format `epoch=EPOCH-step=STEP.ckpt`, with `EPOCH` the epoch and `STEP` the training step at which the checkpoint was taken, helping you track progress and select the best model based on validation performance.
+
+<!-- TODO: when checkpointing is made more flexible, update this information -->
diff --git a/docs/getting_started.md b/docs/getting_started.md
index 94f0a308..73cbd5f0 100644
--- a/docs/getting_started.md
+++ b/docs/getting_started.md
@@ -68,9 +68,8 @@ casanovo configure
 ```
 ![`casanovo configure --help`](images/configure-help.svg)
 
-When using Casanovo to sequence peptides from mass spectra or evaluate a previous model's performance, you can change some of the parameters in this file, indicated with "(I)" in the file. 
-The other parameters will not have an effect unless you are training a new Casanovo model.
-
+When using Casanovo to sequence peptides from mass spectra or evaluate a previous model's performance, you can change some of the parameters in the first section of this file.
+Parameters in the second section will not have an effect unless you are training a new Casanovo model.
 
 ### Download model weights
 
diff --git a/docs/index.md b/docs/index.md
index 6248a0a0..2283aeb2 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -15,6 +15,7 @@ hidden: true
 maxdepth: 1
 ---
 Getting Started <getting_started.md>
+File Formats <file_formats.md>
 FAQs <faq.md>
 Contributing <CONTRIBUTING.md>
 Code of Conduct <CODE_OF_CONDUCT.md>