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--- |
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license: other |
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language: |
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- vi |
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model_name: PhoGPT 7B5 Instruct |
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inference: false |
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model_creator: VinAI Research |
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model_link: https://huggingface.co/vinai/PhoGPT-7B5-Instruct |
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model_type: mpt |
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pipeline_tag: text-generation |
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quantized_by: nguyenviet |
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base_model: vinai/PhoGPT-7B5-Instruct |
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--- |
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# PhoGPT-7B5-Instruct.GGUF |
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GGUF format files of the model [vinai/PhoGPT-7B5-Instruct](https://huggingface.co/vinai/PhoGPT-7B5-Instruct). |
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## Model Details |
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For detailed information about the original model, please refer to [phoGPT's repository](https://github.com/VinAIResearch/PhoGPT). |
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## Uses |
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Select and download the quantization version that fits the needs. |
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## License |
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PhoGPT is licensed under the [PhoGPT Community License](https://github.com/VinAIResearch/PhoGPT/blob/main/LICENSE), Copyright (c) VinAI. All Rights Reserved. |
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## Provided files |
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| Name | Quant method | Size | Use case | |
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| ---- | ---- | ---- | ----- | |
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| [PhoGPT-7B5-Instruct-q2_k.gguf](https://huggingface.co/nguyenviet/PhoGPT-7B5-Instruct-GGUF/blob/main/PhoGPT-7B5-Instruct-q2_k.gguf) | Q2_K | 3.8 GB | smallest, significant quality loss - not recommended for most purposes | |
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| [PhoGPT-7B5-Instruct-q3_k_s.gguf](https://huggingface.co/nguyenviet/PhoGPT-7B5-Instruct-GGUF/blob/main/PhoGPT-7B5-Instruct-q3_k_s.gguf) | Q3_K_S | 4.07 GB | very small, high quality loss | |
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| [PhoGPT-7B5-Instruct-q3_k_m.gguf](https://huggingface.co/nguyenviet/PhoGPT-7B5-Instruct-GGUF/blob/main/PhoGPT-7B5-Instruct-q3_k_m.gguf) | Q3_K_M | 4.66 GB | very small, high quality loss | |
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| [PhoGPT-7B5-Instruct-q3_k_l.gguf](https://huggingface.co/nguyenviet/PhoGPT-7B5-Instruct-GGUF/blob/main/PhoGPT-7B5-Instruct-q3_k_l.gguf) | Q3_K_L | 4.98 GB | small, substantial quality loss | |
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| [PhoGPT-7B5-Instruct-q4_0.gguf](https://huggingface.co/nguyenviet/PhoGPT-7B5-Instruct-GGUF/blob/main/PhoGPT-7B5-Instruct-q4_0.gguf) | Q4_0 | 5.06 GB | legacy; small, very high quality loss - prefer using Q3_K_M | |
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| [PhoGPT-7B5-Instruct-q4_k_s.gguf](https://huggingface.co/nguyenviet/PhoGPT-7B5-Instruct-GGUF/blob/main/PhoGPT-7B5-Instruct-q4_k_s.gguf) | Q4_K_S | 5.1 GB | small, greater quality loss | |
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| [PhoGPT-7B5-Instruct-q4_k_m.gguf](https://huggingface.co/nguyenviet/PhoGPT-7B5-Instruct-GGUF/blob/main/PhoGPT-7B5-Instruct-q4_k_m.gguf) | Q4_K_M | 5.54 GB | medium, balanced quality - recommended | |
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| [PhoGPT-7B5-Instruct-q4_1.gguf](https://huggingface.co/nguyenviet/PhoGPT-7B5-Instruct-GGUF/blob/main/PhoGPT-7B5-Instruct-q4_1.gguf) | Q4_1 | 5.53 GB | legacy; higher accuracy than Q4_0 but not as high as Q5_0, however has quicker inference than Q5 models. |
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| [PhoGPT-7B5-Instruct-q5_0.gguf](https://huggingface.co/nguyenviet/PhoGPT-7B5-Instruct-GGUF/blob/main/PhoGPT-7B5-Instruct-q5_0.gguf) | Q5_0 | 6 GB | legacy; medium, balanced quality - prefer using Q4_K_M | |
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| [PhoGPT-7B5-Instruct-q5_k_s.gguf](https://huggingface.co/nguyenviet/PhoGPT-7B5-Instruct-GGUF/blob/main/PhoGPT-7B5-Instruct-q5_k_s.gguf) | Q5_K_S | 6 GB | large, low quality loss - recommended | |
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| [PhoGPT-7B5-Instruct-q5_k_m.gguf](https://huggingface.co/nguyenviet/PhoGPT-7B5-Instruct-GGUF/blob/main/PhoGPT-7B5-Instruct-q5_k_m.gguf) | Q5_K_M | 6.35 GB | large, very low quality loss - recommended | |
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| [PhoGPT-7B5-Instruct-q5_1.gguf](https://huggingface.co/nguyenviet/PhoGPT-7B5-Instruct-GGUF/blob/main/PhoGPT-7B5-Instruct-q5_1.gguf) | Q5_1 | 6.46 GB | legacy; even higher accuracy, resource usage and slower inference. |
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| [PhoGPT-7B5-Instruct-q6_k.gguf](https://huggingface.co/nguyenviet/PhoGPT-7B5-Instruct-GGUF/blob/main/PhoGPT-7B5-Instruct-q6_k.gguf) | Q6_K | 6.99 GB | very large, extremely low quality loss | |
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| [PhoGPT-7B5-Instruct-q8_0.gguf](https://huggingface.co/nguyenviet/PhoGPT-7B5-Instruct-GGUF/blob/main/PhoGPT-7B5-Instruct-q8_0.gguf) | Q8_0 | 9.05 GB | almost indistinguishable from float16. High resource use and slow, not recommended for most users | |
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| [PhoGPT-7B5-Instruct-f16.gguf](https://huggingface.co/nguyenviet/PhoGPT-7B5-Instruct-GGUF/blob/main/PhoGPT-7B5-Instruct-f16.gguf) | float16 | 17 GB | very large, extremely low quality loss - not recommended | |
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