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.