PEFT Doctor

PEFT Doctor provides pre-flight checks and repair tools for LoRA and QLoRA fine-tuning projects using PEFT, Transformers, TRL, bitsandbytes, and Accelerate. It reviews training settings, datasets, memory estimates, adapters, checkpoints, generation configuration, and distributed setup before or after a run.

Checks report the rules that matched and suggested follow-up actions. They help identify common configuration and data problems; they do not predict model quality or guarantee a successful training run.

python -m pip install -U peft-doctor
peft-doctor check train.py --dataset data.jsonl --model llama-3-8b --explain
peft-doctor fix --input train.py --dry-run

The base install provides the CLI and file-based checks. Install the optional training stack for commands that load model or tokenizer metadata or inspect a live model:

python -m pip install -U "peft-doctor[ml]"

Start Here

For an existing project, run these commands in order:

# 1. Check the environment, script, and dataset.
peft-doctor env
peft-doctor check train.py --dataset data.jsonl --model llama-3-8b --explain

# 2. Preview supported edits. This does not change the file.
peft-doctor fix --input train.py --dry-run

# 3. Write a separate patched script after reviewing the preview.
peft-doctor fix --input train.py --output train.fixed.py

# 4. Check the data and estimate memory.
peft-doctor dataset-doctor data.jsonl --sequence-length 2048
peft-doctor estimate --model llama-3-8b --seq-len 2048 --batch-size 1 --qlora --target-vram 16

For a new project, use the wizard or copy a recipe:

peft-doctor init --model llama3 --gpu T4 --dataset-type chat --target-vram 16 --output-dir my-run
peft-doctor recipe qwen2-qlora-colab --copy qwen-run
peft-doctor validate-recipe qwen-run

What The Results Mean

Every check returns one of four severities:

Severity	Meaning
ERROR	A checked contradiction or invalid artifact was found.
WARNING	A setting or artifact needs review.
OK	The named check passed for the inputs examined.
INFO	Context, a limitation, or a manual follow-up.

Reports can be printed as a table, JSON, or Markdown. The main check command can also write HTML and PDF:

peft-doctor check train.py --dataset data.jsonl --output json
peft-doctor check train.py --dataset data.jsonl --html-report report.html --pdf-report report.pdf

Auto-Repair

fix supports Python scripts, JSON configs, and JSON/JSONL datasets.

# Preview a training-script patch.
peft-doctor fix --input train.py --dry-run

# Write to a new path.
peft-doctor fix --input train.py --output train.fixed.py --family llama

# Preview config changes.
peft-doctor fix --config config.json --dry-run

# Mask pad-token labels in place after making a backup.
peft-doctor fix --dataset data.jsonl --write --pad-token-id 0

Supported script and config repairs include:

• add tokenizer.pad_token = tokenizer.eos_token when no pad-token setup is present;
• set model.config.use_cache = False when gradient checkpointing is enabled;
• turn off fp16 when fp16 and bf16 are both enabled;
• replace high-risk batch size or sequence length literals;
• add warmup_ratio, logging_steps, and save_strategy where supported;
• replace missing or risky LoRA target module lists with a selected model-family profile;
• flag lm_head and embedding targets for manual review;
• set label values to -100 at the supplied pad-token positions.

The fixer is intentionally narrow. It will report changes it cannot make safely instead of rewriting arbitrary Python.

Dataset Checks

Use dataset-doctor for structure and training-format checks:

peft-doctor dataset-doctor data.jsonl --sequence-length 2048

Use dataset-intel for row counts, duplicates, empty assistant messages, assistant-only conversations, malformed data, phrase-based review markers, and character-set distribution:

peft-doctor dataset-intel data.jsonl --limit 5000
peft-doctor dataset-report data.jsonl --output dataset-report.html

The phrase checks are review filters. They do not determine whether an answer is true, hallucinated, malicious, or safe.

Check tokenized labels directly when a dataset contains input_ids and labels:

peft-doctor label-audit tokenized.jsonl --pad-token-id 0

This reports length mismatches, rows where every label is -100, rows with no ignored labels, unmasked pad positions, and the measured supervised-token ratio.

Check train/eval leakage with exact matching and token-set Jaccard similarity:

peft-doctor leakage-check train.jsonl eval.jsonl --threshold 0.90 --limit 10000

TRL And Chat Templates

Check an SFTConfig saved as JSON or YAML against the dataset:

peft-doctor sft-check sft-config.yaml \
  --dataset data.jsonl \
  --chat-template chat-template.jinja

The command checks assistant_only_loss, completion_only_loss, packing strategy, padding-free attention requirements, maximum length, dataset shape, and {% generation %} markers.

Check a template by itself or compare its role handling with a dataset:

peft-doctor chat-template-check tokenizer_config.json \
  --dataset chat.jsonl \
  --assistant-only-loss

LoRA And QLoRA Configuration

Generate target modules or a starter configuration:

peft-doctor targets --model meta-llama/Llama-3-8B
peft-doctor targets --family gpt2 --no-mlp
peft-doctor safe-config --family qwen --output json

Check a current LoraConfig or adapter_config.json:

peft-doctor lora-config-check adapter_config.json
peft-doctor lora-config-check lora.yaml --compiled --hotswap

This covers rank, alpha, dropout, all-linear, risky embedding targets, trainable_token_indices, weight tying, aLoRA merge limitations, QALoRA group size, initialization mode, and target_parameters with compiled hotswap.

Check a bitsandbytes configuration:

peft-doctor quantization-check bnb-config.json --gpu T4 --training

This catches simultaneous 4-bit and 8-bit loading, reviews NF4 for 4-bit training, checks explicit compute dtype, flags T4/bf16 plans, and compares FSDP storage and model dtypes when both are present.

Memory, Runtime, And Cost

Estimate VRAM before model loading:

peft-doctor estimate \
  --model llama-3-8b \
  --seq-len 2048 \
  --batch-size 1 \
  --qlora \
  --target-vram 16

The VRAM estimate is formula-based. Framework allocations, kernels, padding, optimizer choice, and model architecture can change the real peak.

If the model name does not contain its size, pass it explicitly. Architecture overrides are useful for an unrecognized family:

peft-doctor estimate --model ./local-model --params-billion 8 --hidden-size 4096 --num-layers 32 --qlora

Show the same estimate by training phase:

peft-doctor memory-timeline --model llama-3-8b --seq-len 4096 --batch-size 1 --qlora

Run a planning simulation without loading a model:

peft-doctor simulate \
  --model llama-3-8b \
  --dataset data.jsonl \
  --gpu T4 \
  --tokens-per-second 750 \
  --epochs 3 \
  --checkpoint-gb 0.8 \
  --save-total-limit 2 \
  --disk-free-gb 20

Runtime is shown only when measured tokens per second is supplied. Disk use is shown only when checkpoint size is supplied.

Calculate cloud cost from provider prices and throughput you measured on the same workload:

peft-doctor estimate-cost \
  --model llama-3-8b \
  --dataset-size 8000 \
  --seq-len 2048 \
  --epochs 3 \
  --offer "L4:0.80:750" \
  --offer "A100:1.90:1800"

Each offer is NAME:HOURLY_RATE:TOKENS_PER_SECOND. PEFT Doctor does not ship a price table because provider rates and measured throughput change.

Adapters, Hotswap, And Merge

Inspect a saved adapter before upload or merge:

peft-doctor inspect-adapter ./adapter --base-model meta-llama/Llama-3-8B
peft-doctor adapter-integrity ./adapter --write-checksums adapter_checksums.json
peft-doctor adapter-tensors ./adapter --output json

adapter-tensors reads safetensors headers without loading tensor data. It reports exact tensor count, parameter count, dtype counts, tensor ranks, and file bytes.

Compare two adapters using their configuration and actual local files:

peft-doctor compare-adapters ./adapter-r16 ./adapter-r64

The comparison does not predict which adapter is better. Use held-out evaluation metrics for that decision.

Check PEFT hotswap compatibility:

peft-doctor hotswap-check ./adapter-loaded ./adapter-next
peft-doctor hotswap-check ./adapter-loaded ./adapter-next --compiled --prepared-max-rank 64

Before a final merge, inspect the plan:

peft-doctor adapter-check \
  --base-model meta-llama/Llama-3-8B \
  --adapter ./adapter \
  --output-dir ./merged

Run the merge only after the plan is clean:

peft-doctor merge-adapter \
  --base-model meta-llama/Llama-3-8B \
  --adapter ./adapter \
  --output-dir ./merged \
  --revision MODEL_COMMIT_SHA \
  --dtype bf16

The merge command does not need a token for local public artifacts. For a private or gated model, log in with your own Hugging Face account outside the script. --revision accepts a branch, tag, or commit SHA; a commit SHA makes the base-model and tokenizer download reproducible.

Checkpoints And Reproducibility

Find the latest structurally valid checkpoint:

peft-doctor checkpoint-list ./outputs

Inspect one checkpoint and compare it with current settings:

peft-doctor checkpoint-check ./outputs/checkpoint-500
peft-doctor resume-check ./outputs/checkpoint-500 --config training-config.json

Record and verify project files, package versions, platform details, and Git commit:

peft-doctor manifest . --output run-manifest.json
peft-doctor verify-manifest run-manifest.json

Distributed Training

Use the general Accelerate/DeepSpeed checker:

peft-doctor distributed-check accelerate.yaml --training-args training.json

Use the FSDP-specific checker for state dictionaries, CPU-efficient loading, synchronized module state, auto-wrapping, activation checkpointing, process count, and compile settings:

peft-doctor fsdp-check accelerate-fsdp.yaml --training-args training.json

Logs And Notebooks

Scan a Trainer log for NaN/Inf loss, gradient spikes, CUDA OOM, illegal memory access, disk errors, device mismatch, shape mismatch, and overlong input warnings:

peft-doctor analyze-log trainer.log
peft-doctor monitor trainer.log

monitor reports parsed loss direction and a local CUDA memory snapshot when Torch can read one. It does not assign a probability to future NaN loss.

Check a notebook before running it in Colab or Jupyter:

peft-doctor notebook-check model_merge.ipynb

The notebook checker looks for pasted Hugging Face token patterns, tokens passed in shell commands, brittle install cells, and quantized adapter-merge plans.

Recipes

Five complete projects are included:

• llama3-qlora-colab
• qwen2-qlora-colab (alias: qwen-low-vram)
• mistral-lora-local
• gemma-low-vram
• completion-only-sft

Copy and validate one:

peft-doctor recipe llama3-qlora-colab --copy ./my-run
peft-doctor validate-recipe ./my-run
cd my-run
python train.py --dry-run
python train.py --revision MODEL_COMMIT_SHA --max-steps 10

Each project contains README.md, train.py, requirements.txt, sample_data.jsonl, expected_output.md, and tested_environment.md.

Investigation And Planning Commands

These commands add local evidence review for causes that are hard to spot from a single config warning:

peft-doctor investigate ./run
peft-doctor inspect-objective data.jsonl --row 42
peft-doctor gradients gradient-summary.json
peft-doctor minimize failure.json --allow-execution --budget-runs 10
peft-doctor conflicts data.jsonl
peft-doctor coverage data.jsonl --tasks tasks.yaml
peft-doctor privacy data.jsonl --redacted-output data.redacted.jsonl
peft-doctor strategy requirements.yaml
peft-doctor plan --model llama-3-8b --vram 16 --hours 4
peft-doctor compose-plan ./adapter-a ./adapter-b
peft-doctor serving-check ./adapter --engine vllm
peft-doctor next-experiment ./my-run
peft-doctor investigate-distributed ./rank-logs
peft-doctor assistant ./my-run --question "Why did training stop?"

The investigation guide describes each command's inputs, examples, and limits. The failure minimizer runs a user-supplied process only when --allow-execution is set; it uses an argument array and enforces a run budget. The evidence assistant searches local files without calling a remote model.

Command	Purpose	Example
investigate	Correlate dataset, script, and log findings	peft-doctor investigate ./run --output markdown
inspect-objective	Review a row's saved text, chat turns, IDs, and labels	peft-doctor inspect-objective data.jsonl --row 42
gradients	Inspect a JSON summary of trainable gradients and updates	peft-doctor gradients gradient-summary.json
minimize	Reduce a reproducible JSONL failure under a run budget	peft-doctor minimize failure.json --allow-execution --budget-runs 10
conflicts	Find similar questions with differing answers for review	peft-doctor conflicts data.jsonl --threshold 0.82
coverage	Compare data against task markers supplied in YAML/JSON	peft-doctor coverage data.jsonl --tasks tasks.yaml
privacy	Scan common sensitive patterns and optionally redact a copy	peft-doctor privacy data.jsonl --redacted-output clean.jsonl
strategy	Compare prompt, retrieval, and fine-tuning considerations	peft-doctor strategy requirements.yaml
plan	Compare a formula-based memory estimate with supplied budgets	peft-doctor plan --model llama-3-8b --vram 16 --hours 4
compose-plan	Compare local adapter metadata before evaluation	peft-doctor compose-plan ./adapter-a ./adapter-b
serving-check	Review adapter metadata relevant to serving	peft-doctor serving-check ./adapter --engine vllm
next-experiment	Suggest a comparison from notes saved by history	peft-doctor history ./my-run --add-status oom --note "Reduced sequence length" then peft-doctor next-experiment ./my-run
investigate-distributed	Find common error signatures in per-rank logs	peft-doctor investigate-distributed ./rank-logs
assistant	Retrieve matching lines from local run files for a question	peft-doctor assistant ./run --question "Why did training stop?"

These commands inspect local inputs and provide planning guidance. They do not evaluate model quality. inspect-objective does not reproduce a trainer's full collator and packing path; coverage checks user-supplied markers; and privacy uses pattern matching that may miss sensitive values. See the detailed guide for input formats and limitations.

All Commands

Examples operate on the named inputs. Some commands write reports or repaired copies; merge and Hub operations can download or upload model artifacts when configured.

Command	What to use it for	Example
check	Main script, model, dataset, trainer, tokenizer, memory, and risk check	peft-doctor check train.py --dataset data.jsonl --model llama-3-8b --revision MODEL_COMMIT_SHA --explain
fix	Preview or write supported script, config, and dataset repairs	peft-doctor fix --input train.py --output train.fixed.py
estimate	Formula-based VRAM planning	peft-doctor estimate --model llama-3-8b --seq-len 2048 --batch-size 1 --qlora
init	Generate a complete project from model, GPU, dataset type, and VRAM	peft-doctor init --model qwen2 --gpu L4 --dataset-type chat --target-vram 24 --output-dir run
diagnose	Prioritize local script, dataset, model, and memory findings	peft-doctor diagnose train.py --dataset data.jsonl --model llama-3-8b --gpu T4
simulate	Check VRAM, evaluation, runtime inputs, and checkpoint schedule without training	peft-doctor simulate --model llama-3-8b --dataset data.jsonl --gpu T4
memory-timeline	Break the VRAM formula into load, forward, backward, and optimizer phases	peft-doctor memory-timeline --model llama-3-8b --qlora
estimate-cost	Calculate cost from supplied rate and measured throughput	peft-doctor estimate-cost --model llama-3-8b --dataset-size 8000 --offer "L4:0.80:750"
advise-hparams	Get a rule-based rank, alpha, and dropout starting point	peft-doctor advise-hparams --model llama-3-8b --dataset-size 8000 --gpu-vram 24
monitor	Summarize log failures, loss direction, and local GPU memory	peft-doctor monitor trainer.log
auto-tune	Lower estimated memory while preserving effective batch	peft-doctor auto-tune --model llama-3-8b --batch-size 4 --grad-accum 1 --target-vram 16
score	Summarize checked errors and warnings with the documented rule score	peft-doctor score train.py --dataset data.jsonl --model llama-3-8b --gpu T4
dataset-intel	Count dataset rule matches and rows without flagged issues	peft-doctor dataset-intel data.jsonl
dataset-report	Write a static HTML dataset report	peft-doctor dataset-report data.jsonl --output dataset-report.html
dataset-doctor	Check rows, formats, roles, duplicates, lengths, and labels	peft-doctor dataset-doctor data.jsonl --sequence-length 2048
inspect-dataset	Print a compact dataset pre-flight report	peft-doctor inspect-dataset data.jsonl
leakage-check	Find exact and high-overlap train/eval pairs	peft-doctor leakage-check train.jsonl eval.jsonl --threshold 0.9
label-audit	Inspect pretokenized supervision and pad masking	peft-doctor label-audit tokenized.jsonl --pad-token-id 0
sft-check	Check TRL selective loss, packing, padding-free mode, and dataset format	peft-doctor sft-check sft.yaml --dataset data.jsonl --chat-template template.jinja
chat-template-check	Check template variables, roles, and generation markers	peft-doctor chat-template-check tokenizer_config.json --dataset chat.jsonl --assistant-only-loss
lora-config-check	Check current PEFT LoRA fields and variant combinations	peft-doctor lora-config-check adapter_config.json
quantization-check	Check bitsandbytes 4/8-bit, NF4, dtype, and FSDP settings	peft-doctor quantization-check bnb.json --gpu T4
investigate	Join project, data, and log findings into possible causal chains	peft-doctor investigate ./run --output markdown
inspect-objective	Inspect stored text, conversation turns, and label masks for one row	peft-doctor inspect-objective data.jsonl --row 42
gradients	Find zero/nonfinite gradients, zero updates, and optimizer omissions from a JSON snapshot	peft-doctor gradients gradient-summary.json
minimize	Reduce a reproducible dataset failure with explicit permission and a run budget	peft-doctor minimize failure.json --allow-execution --budget-runs 10
conflicts	Find similar prompts with different answers for human review	peft-doctor conflicts data.jsonl --threshold 0.82
coverage	Compare user-supplied task/language markers and conversation patterns with the dataset	peft-doctor coverage data.jsonl --tasks tasks.yaml
privacy	Flag common sensitive-data patterns and optionally write a redacted copy	peft-doctor privacy data.jsonl --redacted-output clean.jsonl
strategy	Give rule-based prompting, retrieval, or fine-tuning guidance from requirements	peft-doctor strategy requirements.yaml
plan	Compare supplied VRAM, disk, GPU count, time, and measured throughput inputs	peft-doctor plan --model llama-3-8b --vram 16 --hours 4 --gpu-count 2
compose-plan	Compare local adapter metadata before a separately evaluated composition	peft-doctor compose-plan ./adapter-a ./adapter-b
serving-check	Review adapter metadata that can affect a named serving engine	peft-doctor serving-check ./adapter --engine vllm
next-experiment	Review local run history and identify missing comparison evidence	peft-doctor history ./my-run --add-status oom --note "Reduced sequence length" then peft-doctor next-experiment ./my-run
investigate-distributed	Scan per-rank log files for common distributed failure signatures	peft-doctor investigate-distributed ./rank-logs
assistant	Retrieve matching lines from local project files for a question	peft-doctor assistant ./my-run --question "Why did training stop?"
lora-efficiency	Review rank and target coverage without quality guessing	peft-doctor lora-efficiency --model llama-3-8b --rank 32
compare-adapters	Compare two local adapter configs and actual file metadata	peft-doctor compare-adapters ./adapter-a ./adapter-b
inspect-adapter	Check adapter config, weights, and base-model metadata	peft-doctor inspect-adapter ./adapter --base-model org/model
adapter-integrity	Validate safetensors structure and write/verify SHA-256 hashes	peft-doctor adapter-integrity ./adapter --write-checksums adapter_checksums.json
adapter-tensors	Count exact tensors, parameters, dtypes, and file bytes	peft-doctor adapter-tensors ./adapter --output json
hotswap-check	Compare two adapters before PEFT hotswap	peft-doctor hotswap-check ./loaded ./incoming --compiled --prepared-max-rank 64
adapter-check	Validate an adapter merge plan without loading the model	peft-doctor adapter-check --base-model org/model --adapter ./adapter --output-dir ./merged
merge-adapter	Merge a LoRA adapter into its base model	peft-doctor merge-adapter --base-model org/model --revision MODEL_COMMIT_SHA --adapter ./adapter --output-dir ./merged
checkpoint-list	Inventory every checkpoint and select the latest structurally valid one	peft-doctor checkpoint-list ./outputs
checkpoint-check	Inspect one Trainer/Accelerate checkpoint	peft-doctor checkpoint-check ./outputs/checkpoint-500
resume-check	Compare checkpoint metadata with current settings	peft-doctor resume-check ./outputs/checkpoint-500 --config config.json
training-plan	Calculate effective batch, optimizer steps, warmup, and saves	peft-doctor training-plan --dataset-size 8000 --batch-size 1 --grad-accum 8 --epochs 3
disk-check	Compare real free disk with an explicit checkpoint budget	peft-doctor disk-check ./outputs --checkpoint-gb 1.2 --save-total-limit 2 --reserve-gb 5
token-lengths	Measure tokenizer-specific or approximate length percentiles	peft-doctor token-lengths data.jsonl --model Qwen/Qwen2.5-7B --revision MODEL_COMMIT_SHA --max-length 2048
distributed-check	Check Accelerate and DeepSpeed precision, process, and offload conflicts	peft-doctor distributed-check accelerate.yaml --training-args training.json
fsdp-check	Check FSDP-specific loading, wrapping, state, and compile settings	peft-doctor fsdp-check accelerate-fsdp.yaml --training-args training.json
generation-check	Validate EOS, padding, lengths, sampling, and beam settings	peft-doctor generation-check ./merged-model
analyze-log	Scan and explain common training-log failures	peft-doctor analyze-log trainer.log
scan-log	Print the lower-level log scan	peft-doctor scan-log trainer.log --output markdown
notebook-check	Check a Colab/Jupyter notebook	peft-doctor notebook-check train.ipynb
scan-notebook	Run the same notebook scanner under its original command name	peft-doctor scan-notebook train.ipynb
profiles	Show a built-in model-family profile	peft-doctor profiles qwen
targets	Recommend LoRA target module names	peft-doctor targets --model Qwen/Qwen2.5-7B
safe-config	Print starter LoRA, QLoRA, and Trainer values	peft-doctor safe-config --family llama --output json
recipe	Print a config recipe or copy a runnable project	peft-doctor recipe gemma-low-vram --copy ./gemma-run
validate-recipe	Confirm all required recipe files and values are present	peft-doctor validate-recipe ./gemma-run
benchmark	Run the packaged pre-flight validation scenario for a recipe	peft-doctor benchmark --recipe llama3-qlora-colab
validate	Write a local Markdown validation report	peft-doctor validate --model qwen --dataset sample.jsonl --report report.md
manifest	Create a reproducibility manifest	peft-doctor manifest . --output run-manifest.json
verify-manifest	Detect changed files and package-version drift	peft-doctor verify-manifest run-manifest.json
upgrade-suggestions	Compare installed versions with declared package minimums	peft-doctor upgrade-suggestions
gpu-fingerprint	Show local GPU identity or a built-in reference VRAM note	peft-doctor gpu-fingerprint "RTX 4090"
history	Append or read local experiment notes	peft-doctor history . --add-status completed --metric "eval_loss=1.82"
knowledge-base	Search bundled troubleshooting rules	peft-doctor knowledge-base "CUDA illegal memory access"
chat	Match a question with local rules plus optional dataset/log checks	peft-doctor chat "Why is my loss exploding?" --dataset data.jsonl --log trainer.log
optimize	Combine fixer, dataset, memory, and report checks	peft-doctor optimize . --html-report optimize-report.html
audit	Enforce a local team policy file	peft-doctor audit . --policy peft-policy.yml
cloud	Show the hosted-service roadmap; it uploads nothing	peft-doctor cloud
env	Show Python, CUDA, Colab, and package status	peft-doctor env --output json
colab	Print a Colab installation and pre-flight cell	peft-doctor colab
version	Print the installed package version	peft-doctor version

The detailed option guide is in docs/commands.md. All documentation links are absolute so they work from both GitHub and PyPI.

Python API

from peft_doctor import diagnose_peft

report = diagnose_peft(
    model=model,
    tokenizer=tokenizer,
    peft_config=peft_config,
    training_args=training_args,
    train_dataset=train_dataset,
)

for issue in report.sorted_issues():
    print(issue.severity, issue.code, issue.message)

Create starter configs:

from peft_doctor import (
    create_safe_bnb_config,
    create_safe_lora_config,
    create_safe_training_args,
)

peft_config = create_safe_lora_config(model_name="meta-llama/Llama-3-8B")
bnb_config = create_safe_bnb_config()
training_args = create_safe_training_args()

Guard Trainer logs:

from peft_doctor import NanLossGuard

guard = NanLossGuard()
for issue in guard.update({"loss": float("nan"), "grad_norm": 145.0}):
    print(issue.code, issue.fix)

Colab

In a fresh notebook cell:

%pip install -q -U "peft-doctor[ml]"

Then run:

!peft-doctor env
!peft-doctor dataset-doctor /content/data.jsonl
!peft-doctor estimate --model llama-3-8b --seq-len 2048 --batch-size 1 --qlora --target-vram 16

Do not paste an access token into a notebook, command, config, or repository. Use your own Colab Secret, environment variable, or interactive Hugging Face login for private and gated models.

Scope And Evidence

PEFT Doctor performs static checks, bounded dataset sampling, local file validation, and formula-based planning. These checks do not establish model quality, training success, factual correctness, or production readiness. Review each finding against the model, trainer, and dataset in use.

• Compatibility matrix
• Command guide
• Troubleshooting guide
• Operational safety
• Privacy and security
• Contributing

License

PEFT Doctor is released under the MIT License.