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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)
+
+
+LLaMA-Pro-8B - GGUF
+- Model creator: https://huggingface.co/TencentARC/
+- Original model: https://huggingface.co/TencentARC/LLaMA-Pro-8B/
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [LLaMA-Pro-8B.Q2_K.gguf](https://huggingface.co/RichardErkhov/TencentARC_-_LLaMA-Pro-8B-gguf/blob/main/LLaMA-Pro-8B.Q2_K.gguf) | Q2_K | 2.91GB |
+| [LLaMA-Pro-8B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/TencentARC_-_LLaMA-Pro-8B-gguf/blob/main/LLaMA-Pro-8B.IQ3_XS.gguf) | IQ3_XS | 3.22GB |
+| [LLaMA-Pro-8B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/TencentARC_-_LLaMA-Pro-8B-gguf/blob/main/LLaMA-Pro-8B.IQ3_S.gguf) | IQ3_S | 3.39GB |
+| [LLaMA-Pro-8B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/TencentARC_-_LLaMA-Pro-8B-gguf/blob/main/LLaMA-Pro-8B.Q3_K_S.gguf) | Q3_K_S | 3.39GB |
+| [LLaMA-Pro-8B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/TencentARC_-_LLaMA-Pro-8B-gguf/blob/main/LLaMA-Pro-8B.IQ3_M.gguf) | IQ3_M | 3.59GB |
+| [LLaMA-Pro-8B.Q3_K.gguf](https://huggingface.co/RichardErkhov/TencentARC_-_LLaMA-Pro-8B-gguf/blob/main/LLaMA-Pro-8B.Q3_K.gguf) | Q3_K | 3.8GB |
+| [LLaMA-Pro-8B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/TencentARC_-_LLaMA-Pro-8B-gguf/blob/main/LLaMA-Pro-8B.Q3_K_M.gguf) | Q3_K_M | 3.8GB |
+| [LLaMA-Pro-8B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/TencentARC_-_LLaMA-Pro-8B-gguf/blob/main/LLaMA-Pro-8B.Q3_K_L.gguf) | Q3_K_L | 4.15GB |
+| [LLaMA-Pro-8B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/TencentARC_-_LLaMA-Pro-8B-gguf/blob/main/LLaMA-Pro-8B.IQ4_XS.gguf) | IQ4_XS | 4.2GB |
+| [LLaMA-Pro-8B.Q4_0.gguf](https://huggingface.co/RichardErkhov/TencentARC_-_LLaMA-Pro-8B-gguf/blob/main/LLaMA-Pro-8B.Q4_0.gguf) | Q4_0 | 4.41GB |
+| [LLaMA-Pro-8B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/TencentARC_-_LLaMA-Pro-8B-gguf/blob/main/LLaMA-Pro-8B.IQ4_NL.gguf) | IQ4_NL | 4.44GB |
+| [LLaMA-Pro-8B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/TencentARC_-_LLaMA-Pro-8B-gguf/blob/main/LLaMA-Pro-8B.Q4_K_S.gguf) | Q4_K_S | 4.45GB |
+| [LLaMA-Pro-8B.Q4_K.gguf](https://huggingface.co/RichardErkhov/TencentARC_-_LLaMA-Pro-8B-gguf/blob/main/LLaMA-Pro-8B.Q4_K.gguf) | Q4_K | 4.71GB |
+| [LLaMA-Pro-8B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/TencentARC_-_LLaMA-Pro-8B-gguf/blob/main/LLaMA-Pro-8B.Q4_K_M.gguf) | Q4_K_M | 4.71GB |
+| [LLaMA-Pro-8B.Q4_1.gguf](https://huggingface.co/RichardErkhov/TencentARC_-_LLaMA-Pro-8B-gguf/blob/main/LLaMA-Pro-8B.Q4_1.gguf) | Q4_1 | 4.89GB |
+| [LLaMA-Pro-8B.Q5_0.gguf](https://huggingface.co/RichardErkhov/TencentARC_-_LLaMA-Pro-8B-gguf/blob/main/LLaMA-Pro-8B.Q5_0.gguf) | Q5_0 | 5.37GB |
+| [LLaMA-Pro-8B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/TencentARC_-_LLaMA-Pro-8B-gguf/blob/main/LLaMA-Pro-8B.Q5_K_S.gguf) | Q5_K_S | 5.37GB |
+| [LLaMA-Pro-8B.Q5_K.gguf](https://huggingface.co/RichardErkhov/TencentARC_-_LLaMA-Pro-8B-gguf/blob/main/LLaMA-Pro-8B.Q5_K.gguf) | Q5_K | 5.52GB |
+| [LLaMA-Pro-8B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/TencentARC_-_LLaMA-Pro-8B-gguf/blob/main/LLaMA-Pro-8B.Q5_K_M.gguf) | Q5_K_M | 5.52GB |
+| [LLaMA-Pro-8B.Q5_1.gguf](https://huggingface.co/RichardErkhov/TencentARC_-_LLaMA-Pro-8B-gguf/blob/main/LLaMA-Pro-8B.Q5_1.gguf) | Q5_1 | 5.85GB |
+| [LLaMA-Pro-8B.Q6_K.gguf](https://huggingface.co/RichardErkhov/TencentARC_-_LLaMA-Pro-8B-gguf/blob/main/LLaMA-Pro-8B.Q6_K.gguf) | Q6_K | 6.39GB |
+| [LLaMA-Pro-8B.Q8_0.gguf](https://huggingface.co/RichardErkhov/TencentARC_-_LLaMA-Pro-8B-gguf/blob/main/LLaMA-Pro-8B.Q8_0.gguf) | Q8_0 | 8.27GB |
+
+
+
+
+Original model description:
+---
+license: llama2
+---
+
+# LLaMA-Pro-8B Model Card
+
+## Model Description
+LLaMA-Pro is a progressive version of the original LLaMA model, enhanced by the addition of Transformer blocks. It specializes in integrating both general language understanding and domain-specific knowledge, particularly in programming and mathematics.
+
+## Development and Training
+Developed by Tencent's ARC Lab, LLaMA-Pro is an 8.3 billion parameter model. It's an expansion of LLaMA2-7B, further trained on code and math corpora totaling 80 billion tokens.
+
+## Intended Use
+This model is designed for a wide range of NLP tasks, with a focus on programming, mathematics, and general language tasks. It suits scenarios requiring integration of natural and programming languages.
+
+## Performance
+LLaMA-Pro demonstrates advanced performance across various benchmarks. It outperforms existing models in the LLaMA series in handling diverse tasks, showcasing its capability as an intelligent language agent.
+
+### Overall Performance on Languages, math and code tasks
+
+  | Model | ARC | Hellaswag | MMLU | TruthfulQA | Winogrande | GSM8K | GSM8K-PoT | HumanEval | MBPP | Avg |
+  | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
+  | LLAMA PRO (8B) | 54.10 | 77.94 | 47.88 | 39.04 | 73.95 | 17.89 | 25.42 | 28.66 | 33.20 | 44.2 |
+  | LLaMA2-7B | 53.07 | 78.59 | 46.87 | 38.76 | 74.03 | 14.48 | 17.68 | 13.05 | 20.09 | 39.62 |
+  | CodeLLaMA-7B | 39.93 | 60.80 | 31.12 | 37.82 | 64.01 | 5.16 | 25.20 | 33.50 | 41.40 | 37.66 |
+  | LLAMA PRO-INSTRUCT | 52.30 | 76.88 | 52.57 | 48.80 | 72.53 | 43.59 | 55.61 | 44.51 | 37.88 | 53.8 |
+
+### Performance on GPT4 Evaluation
+
+| Model | MT Bench |
+| :-: | :-: |
+| Alpaca-13B | 4.53 |
+| CodeLLaMA-7B-Instruct | 5.71 |
+| Vicuna-7B | 6.17 |
+| LLaMA2-7B-Chat | 6.27 |
+| LLAMA PRO-INSTRUCT | 6.32 | 
+
+## Limitations
+While LLaMA-Pro addresses some limitations of previous models in the series, it may still encounter challenges specific to highly specialized domains or tasks.
+
+## Ethical Considerations
+Users should be aware of potential biases in the model and use it responsibly, considering its impact on various applications.
+
+