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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+TinyAlpaca-1.1B - GGUF
+- Model creator: https://huggingface.co/luckychao/
+- Original model: https://huggingface.co/luckychao/TinyAlpaca-1.1B/
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [TinyAlpaca-1.1B.Q2_K.gguf](https://huggingface.co/RichardErkhov/luckychao_-_TinyAlpaca-1.1B-gguf/blob/main/TinyAlpaca-1.1B.Q2_K.gguf) | Q2_K | 0.4GB |
+| [TinyAlpaca-1.1B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/luckychao_-_TinyAlpaca-1.1B-gguf/blob/main/TinyAlpaca-1.1B.IQ3_XS.gguf) | IQ3_XS | 0.44GB |
+| [TinyAlpaca-1.1B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/luckychao_-_TinyAlpaca-1.1B-gguf/blob/main/TinyAlpaca-1.1B.IQ3_S.gguf) | IQ3_S | 0.47GB |
+| [TinyAlpaca-1.1B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/luckychao_-_TinyAlpaca-1.1B-gguf/blob/main/TinyAlpaca-1.1B.Q3_K_S.gguf) | Q3_K_S | 0.47GB |
+| [TinyAlpaca-1.1B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/luckychao_-_TinyAlpaca-1.1B-gguf/blob/main/TinyAlpaca-1.1B.IQ3_M.gguf) | IQ3_M | 0.48GB |
+| [TinyAlpaca-1.1B.Q3_K.gguf](https://huggingface.co/RichardErkhov/luckychao_-_TinyAlpaca-1.1B-gguf/blob/main/TinyAlpaca-1.1B.Q3_K.gguf) | Q3_K | 0.51GB |
+| [TinyAlpaca-1.1B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/luckychao_-_TinyAlpaca-1.1B-gguf/blob/main/TinyAlpaca-1.1B.Q3_K_M.gguf) | Q3_K_M | 0.51GB |
+| [TinyAlpaca-1.1B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/luckychao_-_TinyAlpaca-1.1B-gguf/blob/main/TinyAlpaca-1.1B.Q3_K_L.gguf) | Q3_K_L | 0.55GB |
+| [TinyAlpaca-1.1B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/luckychao_-_TinyAlpaca-1.1B-gguf/blob/main/TinyAlpaca-1.1B.IQ4_XS.gguf) | IQ4_XS | 0.57GB |
+| [TinyAlpaca-1.1B.Q4_0.gguf](https://huggingface.co/RichardErkhov/luckychao_-_TinyAlpaca-1.1B-gguf/blob/main/TinyAlpaca-1.1B.Q4_0.gguf) | Q4_0 | 0.59GB |
+| [TinyAlpaca-1.1B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/luckychao_-_TinyAlpaca-1.1B-gguf/blob/main/TinyAlpaca-1.1B.IQ4_NL.gguf) | IQ4_NL | 0.6GB |
+| [TinyAlpaca-1.1B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/luckychao_-_TinyAlpaca-1.1B-gguf/blob/main/TinyAlpaca-1.1B.Q4_K_S.gguf) | Q4_K_S | 0.6GB |
+| [TinyAlpaca-1.1B.Q4_K.gguf](https://huggingface.co/RichardErkhov/luckychao_-_TinyAlpaca-1.1B-gguf/blob/main/TinyAlpaca-1.1B.Q4_K.gguf) | Q4_K | 0.62GB |
+| [TinyAlpaca-1.1B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/luckychao_-_TinyAlpaca-1.1B-gguf/blob/main/TinyAlpaca-1.1B.Q4_K_M.gguf) | Q4_K_M | 0.62GB |
+| [TinyAlpaca-1.1B.Q4_1.gguf](https://huggingface.co/RichardErkhov/luckychao_-_TinyAlpaca-1.1B-gguf/blob/main/TinyAlpaca-1.1B.Q4_1.gguf) | Q4_1 | 0.65GB |
+| [TinyAlpaca-1.1B.Q5_0.gguf](https://huggingface.co/RichardErkhov/luckychao_-_TinyAlpaca-1.1B-gguf/blob/main/TinyAlpaca-1.1B.Q5_0.gguf) | Q5_0 | 0.71GB |
+| [TinyAlpaca-1.1B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/luckychao_-_TinyAlpaca-1.1B-gguf/blob/main/TinyAlpaca-1.1B.Q5_K_S.gguf) | Q5_K_S | 0.71GB |
+| [TinyAlpaca-1.1B.Q5_K.gguf](https://huggingface.co/RichardErkhov/luckychao_-_TinyAlpaca-1.1B-gguf/blob/main/TinyAlpaca-1.1B.Q5_K.gguf) | Q5_K | 0.73GB |
+| [TinyAlpaca-1.1B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/luckychao_-_TinyAlpaca-1.1B-gguf/blob/main/TinyAlpaca-1.1B.Q5_K_M.gguf) | Q5_K_M | 0.73GB |
+| [TinyAlpaca-1.1B.Q5_1.gguf](https://huggingface.co/RichardErkhov/luckychao_-_TinyAlpaca-1.1B-gguf/blob/main/TinyAlpaca-1.1B.Q5_1.gguf) | Q5_1 | 0.77GB |
+| [TinyAlpaca-1.1B.Q6_K.gguf](https://huggingface.co/RichardErkhov/luckychao_-_TinyAlpaca-1.1B-gguf/blob/main/TinyAlpaca-1.1B.Q6_K.gguf) | Q6_K | 0.84GB |
+| [TinyAlpaca-1.1B.Q8_0.gguf](https://huggingface.co/RichardErkhov/luckychao_-_TinyAlpaca-1.1B-gguf/blob/main/TinyAlpaca-1.1B.Q8_0.gguf) | Q8_0 | 1.09GB |
+
+
+
+
+Original model description:
+---
+language:
+- en
+datasets:
+- tatsu-lab/alpaca
+---
+# Model Card for Model ID
+
+This model checkpoint is the TinyLlama-1.1B fine-tuned on [alpaca dataset](https://huggingface.co/datasets/tatsu-lab/alpaca).
+
+## Model Details
+
+### Model Sources 
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://github.com/jzhang38/TinyLlama
+- **Paper:** [https://arxiv.org/abs/2404.02406]
+
+## Uses
+
+
+The use of this model should comply with the restrictions from [TinyLlama-1.1b](https://github.com/jzhang38/TinyLlama) and 
+[Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca).
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("luckychao/TinyAlpaca-1.1B")
+model = AutoModelForCausalLM.from_pretrained("luckychao/TinyAlpaca-1.1B")
+
+```
+
+## Training Details
+
+### Training Data
+
+We use the [alpaca dataset](https://huggingface.co/datasets/tatsu-lab/alpaca), which is created by researchers from Stanford University.
+
+### Training Procedure
+
+We follow the same training procedure and mostly same hyper-parameters to fine-tune the original Alpaca model on Llama. The procedure can be found in [stanford_alpaca project](https://huggingface.co/datasets/tatsu-lab/alpaca).
+
+#### Training Hyperparameters
+```
+--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 
+
+```
+
+
+## Citation 
+
+The model is mostly developed for the paper below. Please cite it if you find the repository helpful.
+
+**BibTeX:**
+```
+@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}
+}
+```
+
+
+
+
+
+