initial commit

testProject/config.ini

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+[Operation]
+# torch device, falls back to 'cpu' if accel is not available
+device=cuda:0
+# purge existing workdir, adapters and full output when the program starts
+purgeTargetDirectories=false
+
+[Trainer]
+# activate trainer
+train=true
+# save lora adapter to disk after training. Also required for validation and merging
+storeAdapter=true
+# convert text dataset to chat prompts
+trTextDsToChat=true
+# if plain texts have been converted to chat prompts, also keep plain texts in dataset (combined)
+trTextDsKeepPlainText=true
+# path to base model (hf safetensors repo)
+locBaseModel=../../lm/models/safetensors/Qwen2.5-1.5B-Instruct/
+# path to datasets
+locDataset=./testProject/dataset/
+# path: working directory
+locWorkdir=./testProject/work/
+# path: lora adapter output
+locAdapter=./testProject/adapter/
+# training epochs (how many times each dataset entry is trained)
+#   vInplace true: Validation will run after each batch of training epochs until vExpected has been reached, otherwise the next training batch starts
+#   vInplace false: Training is executed once for trEpochs epochs
+trEpochs=10
+# optional int, maximum length of training sequence
+trMaxSeqLength=
+# optional int, training batch size (reduces memory consumption)
+trPerDeviceTrainBatchSize=
+# optional bool, determine training batch size (reduces memory consumption)
+trFindAutoBatchSize=
+# optional int, reduces memory consumption
+trGradientAccSteps=
+# optional bool, reduces memory consumption
+trGradientCheckpointing=
+# optional bool, reduces memory consumption
+trGroupByLength=true
+# optional bool, naively pack traning sequences together (reduces memory consumption)
+trPacking=
+
+[Lora]
+# As bigger the R the more parameters to train
+loraR=32
+# A scaling factor that adjusts the magnitude of the weight matrix. It seems that as higher more weight have the new training
+loraAlpha=8
+# Helps to avoid Overfitting
+loraDropout=0.05
+# lora_only, all, none
+loraBias=none
+# lora task type
+loraTaskType=CAUSAL_LM
+
+[Validation]
+# enable validation
+validate=true
+# Validate in place during training.
+#   In place validation: Validation will run after each batch of training epochs until vExpected has been reached. Requires more memory (both training and grader model are in memory at the same time)
+#   Normal validation: Validation is done once after the traning epochs have been finished
+vInplace=true
+# path: validation dataset
+locValidation=./testProject/validation/
+# path to validation model (hf safetensors repo)
+locGraderModel=../../lm/models/safetensors/Meta-Llama-3.1-8B-Instruct-abliterated/
+# expected percentage of questions passed to pass the validation
+vExpected=80
+# abort processing when validation fails (currently: skip merging to full model). Only relevant for vInplace=false
+vAbortOnFail=true
+# how many times each question is asked and evaluated, averages out evaluation results
+vPasses=3
+# maximum response length for validations
+vGenMaxTokens=60
+# use 4bit quant of the fine tuned model (reduces memory consumption and accuracy), only relevant on gpu. Does not have an effect if vInplace=true, instead of that the training model is used directly
+vQuantModel=false
+# use 4bit quant of the validation model (reduces memory consumption and accuracy), only relevant on gpu
+vQuantGrader=true
+# run grader model always on cpu
+vGraderOnCpu=false
+
+[Merger]
+# merge lora adapter back into the base model
+mergeFull=true
+# path: merged full output
+locFull=./testProject/full/