infosys
/

NT-Java-1.1B-GGUF

@@ -2,78 +2,3 @@
 license: bigcode-openrail-m
 ---
-# CodeLlama 7B - GGUF
-- Model creator: [Infosys](https://huggingface.co/infosys)
-- Original model: [NT-Java-1.1B](https://huggingface.co/infosys/NT-Java-1.1B)
-<!-- description start -->
-## Description
-This repo contains GGUF format model files for [Infosys's NT-Java-1.1B](https://huggingface.co/infosys/NT-Java-1.1B).
-<!-- description end -->
-<!-- README_GGUF.md-about-gguf start -->
-### About GGUF
-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. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.
-Here is an incomplate list of clients and libraries that are known to support GGUF:
-* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
-* [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.
-* [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.
-* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
-* [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.
-* [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.
-* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
-* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
-* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
-<!-- README_GGUF.md-about-gguf end -->
-<!-- prompt-template start -->
-## Prompt template: None
-```
-{prompt}
-```
-<!-- prompt-template end -->
-<!-- compatibility_gguf start -->
-## Explanation of quantisation methods
-<details>
-  <summary>Click to see details</summary>
-The new methods available are:
-* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
-* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
-* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
-* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
-* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
-Refer to the Provided Files table below to see what files use which methods, and how.
-</details>
-<!-- compatibility_gguf end -->
-<!-- README_GGUF.md-provided-files start -->
-## Provided files
-| Name | Quant method | Bits | Size | Max RAM required | Use case |
-| ---- | ---- | ---- | ---- | ---- | ----- |
-| [NT-Java-1.1B_Q3_K_M.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q3_K_M.gguf) | Q3_K_M | 3 | 663 MB| 5.80 GB | very small, high quality loss |
-| [NT-Java-1.1B_Q4_0.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q4_0.gguf) | Q4_0 | 4 | 726 MB| 6.33 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
-| [NT-Java-1.1B_Q4_K_M.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q4_K_M.gguf) | Q4_K_M | 4 | 792 MB| 6.58 GB | medium, balanced quality - recommended |
-| [NT-Java-1.1B_Q5_K_M.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q5_K_M.gguf) | Q5_K_M | 5 | 910 MB| 7.28 GB | large, very low quality loss - recommended |
-| [NT-Java-1.1B_Q6_K.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q6_K.gguf) | Q6_K | 6 | 1.02 GB| 8.03 GB | very large, extremely low quality loss |
-| [NT-Java-1.1B_Q8_0.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q8_0.gguf) | Q8_0 | 8 | 1.32 GB| 9.66 GB | very large, extremely low quality loss - not recommended |
-**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.


2	license: bigcode-openrail-m
3	---
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