Parameter	Value
lora_r	64
lora_alpha	128
lora_dropout	0
task type	CAUSAL_LM
trainable modules	k_proj, o_proj, q_proj, v_proj, shared_expert.gate_proj, shared_expert.up_proj, shared_expert.down_proj, feed_forward.gate_proj, feed_forward.up_proj, feed_forward.down_proj

Training Hyperparameters

Parameter	Value
n_epochs	5
n_evals	5
batch_size	max
learning_rate	0.0001
lr_scheduler_type	cosine
scheduler_num_cycles	0.5
min_lr_ratio	0.1
warmup_ratio	0.05
weight_decay	0.02
max_grad_norm	1
train_on_inputs	false

How to Get Started

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "meta-llama/Llama-4-Scout-17B-16E-Instruct"
adapter = "sidddd625/adaption_finance_local_devnagri_scrip"

tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
model = PeftModel.from_pretrained(model, adapter)

Job Metadata

finetune_job_id: 032b1886-fea8-4c26-bea4-fdec65363a20
training_experiment_id: 8e30f154-c295-47f8-a759-52f799cf36f9
trained_model_name: adaption_finance_local_devnagri_scrip

Framework versions

PEFT 0.15.1