--- pipeline_tag: text-generation inference: false license: apache-2.0 library_name: transformers tags: - language - granite-3.3 - TensorBlock - GGUF base_model: ibm-granite/granite-3.3-2b-instruct ---
TensorBlock
[![Website](https://img.shields.io/badge/Website-tensorblock.co-blue?logo=google-chrome&logoColor=white)](https://tensorblock.co) [![Twitter](https://img.shields.io/twitter/follow/tensorblock_aoi?style=social)](https://twitter.com/tensorblock_aoi) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-5865F2?logo=discord&logoColor=white)](https://discord.gg/Ej5NmeHFf2) [![GitHub](https://img.shields.io/badge/GitHub-TensorBlock-black?logo=github&logoColor=white)](https://github.com/TensorBlock) [![Telegram](https://img.shields.io/badge/Telegram-Group-blue?logo=telegram)](https://t.me/TensorBlock) ## ibm-granite/granite-3.3-2b-instruct - GGUF This repo contains GGUF format model files for [ibm-granite/granite-3.3-2b-instruct](https://huggingface.co/ibm-granite/granite-3.3-2b-instruct). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5165](https://github.com/ggml-org/llama.cpp/commit/1d735c0b4fa0551c51c2f4ac888dd9a01f447985). ## Our projects
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## Prompt template ``` <|start_of_role|>system<|end_of_role|>{system_prompt}<|end_of_text|> <|start_of_role|>user<|end_of_role|>{prompt}<|end_of_text|> <|start_of_role|>assistant<|end_of_role|> ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [granite-3.3-2b-instruct-Q2_K.gguf](https://huggingface.co/tensorblock/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/granite-3.3-2b-instruct-Q2_K.gguf) | Q2_K | 0.978 GB | smallest, significant quality loss - not recommended for most purposes | | [granite-3.3-2b-instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/granite-3.3-2b-instruct-Q3_K_S.gguf) | Q3_K_S | 1.130 GB | very small, high quality loss | | [granite-3.3-2b-instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/granite-3.3-2b-instruct-Q3_K_M.gguf) | Q3_K_M | 1.252 GB | very small, high quality loss | | [granite-3.3-2b-instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/granite-3.3-2b-instruct-Q3_K_L.gguf) | Q3_K_L | 1.357 GB | small, substantial quality loss | | [granite-3.3-2b-instruct-Q4_0.gguf](https://huggingface.co/tensorblock/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/granite-3.3-2b-instruct-Q4_0.gguf) | Q4_0 | 1.453 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [granite-3.3-2b-instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/granite-3.3-2b-instruct-Q4_K_S.gguf) | Q4_K_S | 1.464 GB | small, greater quality loss | | [granite-3.3-2b-instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/granite-3.3-2b-instruct-Q4_K_M.gguf) | Q4_K_M | 1.545 GB | medium, balanced quality - recommended | | [granite-3.3-2b-instruct-Q5_0.gguf](https://huggingface.co/tensorblock/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/granite-3.3-2b-instruct-Q5_0.gguf) | Q5_0 | 1.757 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [granite-3.3-2b-instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/granite-3.3-2b-instruct-Q5_K_S.gguf) | Q5_K_S | 1.757 GB | large, low quality loss - recommended | | [granite-3.3-2b-instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/granite-3.3-2b-instruct-Q5_K_M.gguf) | Q5_K_M | 1.805 GB | large, very low quality loss - recommended | | [granite-3.3-2b-instruct-Q6_K.gguf](https://huggingface.co/tensorblock/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/granite-3.3-2b-instruct-Q6_K.gguf) | Q6_K | 2.081 GB | very large, extremely low quality loss | | [granite-3.3-2b-instruct-Q8_0.gguf](https://huggingface.co/tensorblock/ibm-granite_granite-3.3-2b-instruct-GGUF/blob/main/granite-3.3-2b-instruct-Q8_0.gguf) | Q8_0 | 2.694 GB | very large, extremely low quality loss - not recommended | ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/ibm-granite_granite-3.3-2b-instruct-GGUF --include "granite-3.3-2b-instruct-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: ```shell huggingface-cli download tensorblock/ibm-granite_granite-3.3-2b-instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```