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--- |
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license: llama3 |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- nvidia |
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- chatqa-1.5 |
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- chatqa |
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- llama-3 |
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- pytorch |
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- TensorBlock |
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- GGUF |
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base_model: nvidia/Llama3-ChatQA-1.5-70B |
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--- |
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<div style="width: auto; margin-left: auto; margin-right: auto"> |
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<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> |
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</div> |
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<div style="display: flex; justify-content: space-between; width: 100%;"> |
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<div style="display: flex; flex-direction: column; align-items: flex-start;"> |
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<p style="margin-top: 0.5em; margin-bottom: 0em;"> |
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Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a> |
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</p> |
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</div> |
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</div> |
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## nvidia/Llama3-ChatQA-1.5-70B - GGUF |
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This repo contains GGUF format model files for [nvidia/Llama3-ChatQA-1.5-70B](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-70B). |
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The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d). |
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<div style="text-align: left; margin: 20px 0;"> |
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<a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;"> |
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Run them on the TensorBlock client using your local machine ↗ |
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</a> |
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</div> |
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## Prompt template |
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``` |
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<|begin_of_text|>System: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context. |
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User: {prompt} |
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Assistant: |
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``` |
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## Model file specification |
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| Filename | Quant type | File Size | Description | |
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| -------- | ---------- | --------- | ----------- | |
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| [Llama3-ChatQA-1.5-70B-Q2_K.gguf](https://huggingface.co/tensorblock/Llama3-ChatQA-1.5-70B-GGUF/blob/main/Llama3-ChatQA-1.5-70B-Q2_K.gguf) | Q2_K | 26.375 GB | smallest, significant quality loss - not recommended for most purposes | |
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| [Llama3-ChatQA-1.5-70B-Q3_K_S.gguf](https://huggingface.co/tensorblock/Llama3-ChatQA-1.5-70B-GGUF/blob/main/Llama3-ChatQA-1.5-70B-Q3_K_S.gguf) | Q3_K_S | 30.912 GB | very small, high quality loss | |
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| [Llama3-ChatQA-1.5-70B-Q3_K_M.gguf](https://huggingface.co/tensorblock/Llama3-ChatQA-1.5-70B-GGUF/blob/main/Llama3-ChatQA-1.5-70B-Q3_K_M.gguf) | Q3_K_M | 34.267 GB | very small, high quality loss | |
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| [Llama3-ChatQA-1.5-70B-Q3_K_L.gguf](https://huggingface.co/tensorblock/Llama3-ChatQA-1.5-70B-GGUF/blob/main/Llama3-ChatQA-1.5-70B-Q3_K_L.gguf) | Q3_K_L | 37.141 GB | small, substantial quality loss | |
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| [Llama3-ChatQA-1.5-70B-Q4_0.gguf](https://huggingface.co/tensorblock/Llama3-ChatQA-1.5-70B-GGUF/blob/main/Llama3-ChatQA-1.5-70B-Q4_0.gguf) | Q4_0 | 39.970 GB | legacy; small, very high quality loss - prefer using Q3_K_M | |
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| [Llama3-ChatQA-1.5-70B-Q4_K_S.gguf](https://huggingface.co/tensorblock/Llama3-ChatQA-1.5-70B-GGUF/blob/main/Llama3-ChatQA-1.5-70B-Q4_K_S.gguf) | Q4_K_S | 40.347 GB | small, greater quality loss | |
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| [Llama3-ChatQA-1.5-70B-Q4_K_M.gguf](https://huggingface.co/tensorblock/Llama3-ChatQA-1.5-70B-GGUF/blob/main/Llama3-ChatQA-1.5-70B-Q4_K_M.gguf) | Q4_K_M | 42.520 GB | medium, balanced quality - recommended | |
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| [Llama3-ChatQA-1.5-70B-Q5_0.gguf](https://huggingface.co/tensorblock/Llama3-ChatQA-1.5-70B-GGUF/blob/main/Llama3-ChatQA-1.5-70B-Q5_0.gguf) | Q5_0 | 48.657 GB | legacy; medium, balanced quality - prefer using Q4_K_M | |
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| [Llama3-ChatQA-1.5-70B-Q5_K_S.gguf](https://huggingface.co/tensorblock/Llama3-ChatQA-1.5-70B-GGUF/blob/main/Llama3-ChatQA-1.5-70B-Q5_K_S.gguf) | Q5_K_S | 48.657 GB | large, low quality loss - recommended | |
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| [Llama3-ChatQA-1.5-70B-Q5_K_M.gguf](https://huggingface.co/tensorblock/Llama3-ChatQA-1.5-70B-GGUF/blob/main/Llama3-ChatQA-1.5-70B-Q5_K_M.gguf) | Q5_K_M | 49.950 GB | large, very low quality loss - recommended | |
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| [Llama3-ChatQA-1.5-70B-Q8_0](https://huggingface.co/tensorblock/Llama3-ChatQA-1.5-70B-GGUF/blob/main/Llama3-ChatQA-1.5-70B-Q8_0) | Q6_K | 74.975 GB | very large, extremely low quality loss | |
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| [Llama3-ChatQA-1.5-70B-Q6_K](https://huggingface.co/tensorblock/Llama3-ChatQA-1.5-70B-GGUF/blob/main/Llama3-ChatQA-1.5-70B-Q6_K) | Q8_0 | 57.888 GB | very large, extremely low quality loss - not recommended | |
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## Downloading instruction |
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### Command line |
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Firstly, install Huggingface Client |
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```shell |
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pip install -U "huggingface_hub[cli]" |
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``` |
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Then, downoad the individual model file the a local directory |
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```shell |
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huggingface-cli download tensorblock/Llama3-ChatQA-1.5-70B-GGUF --include "Llama3-ChatQA-1.5-70B-Q2_K.gguf" --local-dir MY_LOCAL_DIR |
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``` |
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If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: |
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```shell |
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huggingface-cli download tensorblock/Llama3-ChatQA-1.5-70B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' |
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``` |
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