Llm Toolkit

@swampadmin/llm-toolkitv2026.02.14.0· 15d agoMODELS·WORKFLOWS

01README

End-to-end LLM fine-tuning pipeline with dataset preparation, distributed training, quantization, and automated evaluation.

Supported runtimes

Runtime	Status
PyTorch	Stable
JAX	Beta
ONNX	Export only

02Release Notes

New

Evaluation model with benchmark, compare, and report methods
Batch inference workflow — load model, run inference, generate report
Dataset augment method for synthetic data generation
Training quantize method for INT8/FP16 model compression

Breaking

training.export now requires a format argument (pytorch, onnx, safetensors)

03Models3

@swampadmin/llm-toolkit/datasetv1.0.0dataset.ts

fn prepare(name: string)

prepare operation

Argument	Type	Required	Description
name	string	yes	Resource name

fn validate(name: string)

validate operation

Argument	Type	Required	Description
name	string	yes	Resource name

fn split(name: string)

split operation

Argument	Type	Required	Description
name	string	yes	Resource name

fn augment(name: string)

augment operation

Argument	Type	Required	Description
name	string	yes	Resource name

Files

dataset.log(text/plain)— Operation audit log

dataset.json(application/json)— Structured output

@swampadmin/llm-toolkit/trainingv1.0.0training.ts

fn fine_tune(name: string)

fine tune operation

Argument	Type	Required	Description
name	string	yes	Resource name

fn resume(name: string)

resume operation

Argument	Type	Required	Description
name	string	yes	Resource name

fn export(name: string)

export operation

Argument	Type	Required	Description
name	string	yes	Resource name

fn quantize(name: string)

quantize operation

Argument	Type	Required	Description
name	string	yes	Resource name

Resources

training.state(persistent)— Managed resource state

training.lock(ephemeral)— Concurrency lock

Files

training.log(text/plain)— Operation audit log

training.json(application/json)— Structured output

@swampadmin/llm-toolkit/evaluationv1.0.0evaluation.ts

fn benchmark(name: string)

benchmark operation

Argument	Type	Required	Description
name	string	yes	Resource name

fn compare(name: string)

compare operation

Argument	Type	Required	Description
name	string	yes	Resource name

fn report(name: string)

report operation

Argument	Type	Required	Description
name	string	yes	Resource name

Files

evaluation.log(text/plain)— Operation audit log

evaluation.json(application/json)— Structured output

04Workflows2

Train and Evaluatetrain-eval

Train and Evaluate workflow

train-eval-jobExecute Train and Evaluate

1.Prepare Dataset@swampadmin/llm-toolkit/dataset.prepare— Prepare Dataset step

2.Fine Tune@swampadmin/llm-toolkit/training.fine_tune— Fine Tune step

3.Evaluate@swampadmin/llm-toolkit/evaluation.benchmark— Evaluate step

Batch Inferencebatch-inference

Batch Inference workflow

batch-inference-jobExecute Batch Inference

1.Load Model@swampadmin/llm-toolkit/training.export— Load Model step

2.Run Inference@swampadmin/llm-toolkit/evaluation.benchmark— Run Inference step

3.Generate Report@swampadmin/llm-toolkit/evaluation.report— Generate Report step

05Previous Versions1

2025.08.05.0Dec 1, 2025

06Stats

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Repository

https://github.com/swamp-club/llm-toolkit

07Platforms

linux · x86_64 linux · aarch64

08Labels

#ml #llm #fine-tuning #ai