Text Generation
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GGUF
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granite
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gguf-my-repo
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  name: pass@1
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- # MoMonir/granite-8b-code-instruct-Q4_K_M-GGUF
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  This model was converted to GGUF format from [`ibm-granite/granite-8b-code-instruct`](https://huggingface.co/ibm-granite/granite-8b-code-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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  Refer to the [original model card](https://huggingface.co/ibm-granite/granite-8b-code-instruct) for more details on the model.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Use with llama.cpp
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  Install llama.cpp through brew.
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  CLI:
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  ```bash
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- llama-cli --hf-repo MoMonir/granite-8b-code-instruct-Q4_K_M-GGUF --model granite-8b-code-instruct.Q4_K_M.gguf -p "The meaning to life and the universe is"
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  ```
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  Server:
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  ```bash
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- llama-server --hf-repo MoMonir/granite-8b-code-instruct-Q4_K_M-GGUF --model granite-8b-code-instruct.Q4_K_M.gguf -c 2048
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  ```
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  Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
 
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  name: pass@1
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  ---
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+ # MoMonir/granite-8b-code-instruct-GGUF
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  This model was converted to GGUF format from [`ibm-granite/granite-8b-code-instruct`](https://huggingface.co/ibm-granite/granite-8b-code-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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  Refer to the [original model card](https://huggingface.co/ibm-granite/granite-8b-code-instruct) for more details on the model.
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+ <!-- README_GGUF.md-about-gguf start -->
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+ ### About GGUF ([TheBloke](https://huggingface.co/TheBloke) Description)
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+ GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
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+ Here is an incomplete list of clients and libraries that are known to support GGUF:
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+ * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
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+ * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
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+ * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
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+ * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
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+ * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
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+ * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
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+ * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
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+ * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
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+ * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
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+ * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
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+ <!-- README_GGUF.md-about-gguf end -->
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  ## Use with llama.cpp
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  Install llama.cpp through brew.
 
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  CLI:
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  ```bash
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+ llama-cli --hf-repo MoMonir/granite-8b-code-instruct-GGUF --model granite-8b-code-instruct.Q4_K_M.gguf -p "The meaning to life and the universe is"
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  ```
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  Server:
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  ```bash
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+ llama-server --hf-repo MoMonir/granite-8b-code-instruct-GGUF --model granite-8b-code-instruct.Q4_K_M.gguf -c 2048
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  ```
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  Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.