Hyperparameters

Lamini tuning supports most hyperparameters in HuggingFace's training arguments, as well as some Lamini-specific options.

These can be set in the tune method:

# code/hyperparameters.py

from lamini import Lamini

llm = Lamini(model_name="meta-llama/Meta-Llama-3.1-8B-Instruct")
data = [
    {
        "input": "What is Lamini? Is it like a robot or a computer program?",
        "output": "Lamini is a program for the execution of LLMs called a large language model engine. It is not a robot, but rather a tool for building and executing LLMs.",
    }
]

results = llm.tune(data_or_dataset_id=data, finetune_args={"learning_rate": 1.0e-4})

See Memory Tuning for use-case specific suggestions.

finetune_args

max_finetuning_examples (int, optional)
Default: size of the dataset
Sets the maximum number of data points for fine-tuning. If not set, the model is fine-tuned on the entire dataset.
max_steps (int, optional)
Default: 100
Specifies the total number of training steps to perform.
This parameter is passed to HuggingFace's Transformers TrainingArguments.
gradient_accumulation_steps (int, optional)
Default: 2
Number of update steps to accumulate the gradients for, before performing a backward/update pass.
Usage note: a higher setting can improve memory efficiency and thus reduce training time, often with a neutral effect on model accuracy.
This parameter is passed to HuggingFace's Transformers TrainingArguments.
learning_rate (float, optional)
Default: 9.0e-4
The initial learning rate for the fine-tuning.
This parameter is passed to HuggingFace's Transformers TrainingArguments.
Usage note: see the Memory Tuning section for tips on setting learning rate.
save_steps (int or float, optional)
Default: 60
Number of update steps between two checkpoint saves.
This parameter is passed to HuggingFace's Transformers TrainingArguments.
max_length (int, optional)
Default: 2048
Specifies the maximum sequence length for the forward pass, acting as the block size for the model.
Should be a power of 2, no larger than 8192.
Usage note: max_length should be at least as large as the size of your datapoints. If training with large datapoints is not converging, increasing this value may help. However, larger values of max_length increase training time, and very large values will exhaust GPU memory. There's often room to reduce the size of your datapoints so a smaller max_length can be used.
optim (str, optional)
Default: "adafactor"
The optimizer to use: adamw_hf, adamw_torch, adamw_torch_fused, adamw_apex_fused, adamw_anyprecision or adafactor.
This parameter is passed to HuggingFace's Transformers TrainingArguments.
r_value (int, optional)
Default: 64
Specifies the size of the LoRA (Low-Rank Adaptation) component.
index_method (str, optional)
Default: "IndexIVFPQ"
The index method used for approximate nearest neighbor search of high-dimensional vectors: IndexIVFPQ, IndexHNSWPQ,IndexHNSWFlat, IndexFlatL2, IndexPQ
index_k (int, optional)
Default: 2
Determines the number of nearest neighbors to consider.
index_max_size (int, optional)
Default: 65536
Maximum size of the index.
index_pq_m (int, optional)
Default: 8
Number of factors of product quantization.
Only used when index_method is IndexIVFPQ or IndexPQ. Ignored otherwise.
index_pq_nbits (int, optional)
Default: 8
Number of bits in which each low-dimensional vector is stored. Range: [1, 16]
Only used when index_method is IndexIVFPQ or IndexPQ. Ignored otherwise.
index_ivf_nlist (int, optional)
Default: 2048
Number of buckets during clustering for IVFLAT.
Only used when index_method is IndexIVFPQ. Ignored otherwise.
index_ivf_nprobe (int, optional)
Default: 48
Number of buckets to search during the first step of IVFLAT.
Only used when index_method is IndexIVFPQ. Ignored otherwise.
index_hnsw_m (int, optional)
Default: 32
Range: [4, 64]. Used in HNSW (Hierarchical Navigable Small World Graph) algorithm.
Only used when index_method is IndexHNSWPQ or HNSWFlat. Ignored otherwise.
index_hnsw_efConstruction (int, optional)
Default: 16
Expansion factor at construction time for HNSW. Range: [8, 512]
Only used when index_method is IndexHNSWPQ or HNSWFlat. Ignored otherwise.
index_hnsw_efSearch (int, optional)
Default: 8
Expansion factor at search time for HNSW.
Only used when index_method is IndexHNSWPQ or HNSWFlat. Ignored otherwise.

Immutables

The following configs are only supported at their default values:

batch_size: 1
early_stopping: false
num_train_epochs: Will be overriden by max_steps
temperature: 0
lora_alpha: 1

gpu_config

gpus: (int, optional)
Default: 1
Number of GPUs per node to use for the tuning job.
nodes: (int, optional)
Default: 1
Number of nodes (machines containing multiple GPUs) to use for the tuning job.

gpu_config = {
    "gpus": 4,
    "nodes": 1,
}

The Lamini On-Demand allows a maximum of GPUs and nodes based on our server availability. If you are on Lamini Reserved or Self-managed, you can specify any number of GPUs and nodes within your provisioned cluster size. Your job will be queued until the requested number of GPUs and nodes is available.

If the required GPUs and nodes are not available, the configuration defaults to the system limit, and the job is queued until the resources become available. When using multiple nodes, specify the number of GPUs required per node.

Examples:

gpu_config = {"gpus": 8, "nodes": 1}  # total 8 GPUs
gpu_config = {"gpus": 8, "nodes": 2}  # total 16 GPUs
gpu_config = {"gpus": 4, "nodes": 2}  # total 8 GPUs
gpu_config = {"gpus": 9, "nodes": 1}  # error, assuming max GPUs per node is 8

data_or_dataset_id

data_or_dataset_id (JSONL or CSV file or dataset ID of an already uploaded dataset, required)
Default: no default
Specifies the dataset to use for the tuning job.