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| Name | Quant method | Size |
|---|---|---|
| TinyAlpaca-1.1B.Q2_K.gguf | Q2_K | 0.4GB |
| TinyAlpaca-1.1B.IQ3_XS.gguf | IQ3_XS | 0.44GB |
| TinyAlpaca-1.1B.IQ3_S.gguf | IQ3_S | 0.47GB |
| TinyAlpaca-1.1B.Q3_K_S.gguf | Q3_K_S | 0.47GB |
| TinyAlpaca-1.1B.IQ3_M.gguf | IQ3_M | 0.48GB |
| TinyAlpaca-1.1B.Q3_K.gguf | Q3_K | 0.51GB |
| TinyAlpaca-1.1B.Q3_K_M.gguf | Q3_K_M | 0.51GB |
| TinyAlpaca-1.1B.Q3_K_L.gguf | Q3_K_L | 0.55GB |
| TinyAlpaca-1.1B.IQ4_XS.gguf | IQ4_XS | 0.57GB |
| TinyAlpaca-1.1B.Q4_0.gguf | Q4_0 | 0.59GB |
| TinyAlpaca-1.1B.IQ4_NL.gguf | IQ4_NL | 0.6GB |
| TinyAlpaca-1.1B.Q4_K_S.gguf | Q4_K_S | 0.6GB |
| TinyAlpaca-1.1B.Q4_K.gguf | Q4_K | 0.62GB |
| TinyAlpaca-1.1B.Q4_K_M.gguf | Q4_K_M | 0.62GB |
| TinyAlpaca-1.1B.Q4_1.gguf | Q4_1 | 0.65GB |
| TinyAlpaca-1.1B.Q5_0.gguf | Q5_0 | 0.71GB |
| TinyAlpaca-1.1B.Q5_K_S.gguf | Q5_K_S | 0.71GB |
| TinyAlpaca-1.1B.Q5_K.gguf | Q5_K | 0.73GB |
| TinyAlpaca-1.1B.Q5_K_M.gguf | Q5_K_M | 0.73GB |
| TinyAlpaca-1.1B.Q5_1.gguf | Q5_1 | 0.77GB |
| TinyAlpaca-1.1B.Q6_K.gguf | Q6_K | 0.84GB |
| TinyAlpaca-1.1B.Q8_0.gguf | Q8_0 | 1.09GB |
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("luckychao/TinyAlpaca-1.1B")
model = AutoModelForCausalLM.from_pretrained("luckychao/TinyAlpaca-1.1B")
--num_train_epochs 3 \
--per_device_train_batch_size 2 \
--per_device_eval_batch_size 2 \
--gradient_accumulation_steps 4 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 1000 \
--save_total_limit 1 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--bf16 True \
--fsdp "full_shard auto_wrap" \
--fsdp_transformer_layer_cls_to_wrap 'LlamaDecoderLayer' \
--model_max_length 2048
@article{hao2024exploring,
title={Exploring Backdoor Vulnerabilities of Chat Models},
author={Hao, Yunzhuo and Yang, Wenkai and Lin, Yankai},
journal={arXiv preprint arXiv:2404.02406},
year={2024}
}