habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-4epochs-oasst1-top1-instruct-V1-GGUF

Quantized GGUF model files for TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-4epochs-oasst1-top1-instruct-V1 from habanoz

Name	Quant method	Size
tinyllama-1.1b-intermediate-step-715k-1.5t-lr-5-4epochs-oasst1-top1-instruct-v1.fp16.gguf	fp16	2.20 GB
tinyllama-1.1b-intermediate-step-715k-1.5t-lr-5-4epochs-oasst1-top1-instruct-v1.q2_k.gguf	q2_k	482.14 MB
tinyllama-1.1b-intermediate-step-715k-1.5t-lr-5-4epochs-oasst1-top1-instruct-v1.q3_k_m.gguf	q3_k_m	549.85 MB
tinyllama-1.1b-intermediate-step-715k-1.5t-lr-5-4epochs-oasst1-top1-instruct-v1.q4_k_m.gguf	q4_k_m	667.81 MB
tinyllama-1.1b-intermediate-step-715k-1.5t-lr-5-4epochs-oasst1-top1-instruct-v1.q5_k_m.gguf	q5_k_m	782.04 MB
tinyllama-1.1b-intermediate-step-715k-1.5t-lr-5-4epochs-oasst1-top1-instruct-v1.q6_k.gguf	q6_k	903.41 MB
tinyllama-1.1b-intermediate-step-715k-1.5t-lr-5-4epochs-oasst1-top1-instruct-v1.q8_0.gguf	q8_0	1.17 GB

Original Model Card:

TinyLlama-1.1B-intermediate-step-715k-1.5T finetuned using OpenAssistant/oasst_top1_2023-08-25 dataset.

Qlora is used. Adapter is merged.

SFT code: https://github.com/habanoz/qlora.git

Command used:

accelerate launch $BASE_DIR/qlora/train.py \
  --model_name_or_path $BASE_MODEL \
  --working_dir $BASE_DIR/$OUTPUT_NAME-checkpoints \
  --output_dir $BASE_DIR/$OUTPUT_NAME-peft \
  --merged_output_dir $BASE_DIR/$OUTPUT_NAME \
  --final_output_dir $BASE_DIR/$OUTPUT_NAME-final \
  --num_train_epochs 4 \
  --logging_steps 1 \
  --save_strategy steps \
  --save_steps 75 \
  --save_total_limit 2 \
  --data_seed 11422 \
  --evaluation_strategy steps \
  --per_device_eval_batch_size 4 \
  --eval_dataset_size 0.01 \
  --eval_steps 75 \
  --max_new_tokens 1024 \
  --dataloader_num_workers 3 \
  --logging_strategy steps \
  --do_train \
  --do_eval \
  --lora_r 64 \
  --lora_alpha 16 \
  --lora_modules all \
  --bits 4 \
  --double_quant \
  --quant_type nf4 \
  --lr_scheduler_type constant \
  --dataset oasst1-top1 \
  --dataset_format oasst1 \
  --model_max_len 1024 \
  --per_device_train_batch_size 4 \
  --gradient_accumulation_steps 4 \
  --learning_rate 1e-5 \
  --adam_beta2 0.999 \
  --max_grad_norm 0.3 \
  --lora_dropout 0.0 \
  --weight_decay 0.0 \
  --seed 11422 \
  --gradient_checkpointing \
  --use_flash_attention_2 \
  --ddp_find_unused_parameters False