README.md

---
base_model: infosys/NT-Java-1.1B-GGUF
widget:
- text: "public class HelloWorld {\n    public static void main(String[] args) {"
  example_title: Hello world
  group: Java
language:
- code
tags:
- NarrowTransformer
license: bigcode-openrail-m
datasets:
- bigcode/starcoderdata
library_name: transformers
model_creator: Infosys
model_name: infosys/NT-Java-1.1B
model_type: gpt_bigcode
prompt_template: |
  {prompt}
quantized_by: Infsys
pipeline_tag: text-generation
---

# NT-Java-1.1B - 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 -->
## Compatibility

These NT-Java-1.1B GGUFs are compatible with llama.cpp from May 29th 2024 onwards.

They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## 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_Q2_K.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q2_K.gguf) | Q2_K | 2 | 511 MB| 764 MB | smallest, significant quality loss - not recommended for most purposes |
| [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| 912 MB | 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| 1021 MB | 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| 1.1 GB | medium, balanced quality - recommended |
| [NT-Java-1.1B_Q5_K.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q5_0.gguf) | Q5_0 | 5 | 868 MB| 1.08 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [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| 1.13 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| 1.24 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| 1.54 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.



<!-- README_GGUF.md-provided-files end -->

<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files

**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.

The following clients/libraries will automatically download models for you, providing a list of available models to choose from:

* LM Studio
* LoLLMS Web UI
* Faraday.dev

### In `text-generation-webui`

Under Download Model, you can enter the model repo: infosys/NT-Java-1.1B-GGUF and below it, a specific filename to download, such as: NT-Java-1.1B_Q4_K_M.gguf.

Then click Download.

### On the command line, including multiple files at once

I recommend using the `huggingface-hub` Python library:

```shell
pip3 install huggingface-hub
```

Then you can download any individual model file to the current directory, at high speed, with a command like this:

```shell
huggingface-cli download infosys/NT-Java-1.1B-GGUF NT-Java-1.1B_Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```

<details>
  <summary>More advanced huggingface-cli download usage (click to read)</summary>

You can also download multiple files at once with a pattern:

```shell
huggingface-cli download infosys/NT-Java-1.1B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```

For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).

To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:

```shell
pip3 install hf_transfer
```

And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:

```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download infosys/NT-Java-1.1B-GGUF NT-Java-1.1B_Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```

Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->

<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command

Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.

```shell
./main -ngl 35 -m NT-Java-1.1B_Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "[INST] {prompt} [/INST]"
```

Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.

If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`

For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)

## How to run in `text-generation-webui`

Further instructions here: [text-generation-webui/docs/04 - Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20-%20Model%20Tab.md#llamacpp).

## How to run from Python code

You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) version 0.2.23 and later.

### How to load this model in Python code, using llama-cpp-python

For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).

#### First install the package

Run one of the following commands, according to your system:

```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python

# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```

#### Simple llama-cpp-python example code

```python
from llama_cpp import Llama

# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
  model_path="./NT-Java-1.1B_Q4_K_M.gguf",  # Download the model file first
  n_ctx=2048,  # The max sequence length to use - note that longer sequence lengths require much more resources
  n_threads=8,            # The number of CPU threads to use, tailor to your system and the resulting performance
  n_gpu_layers=35         # The number of layers to offload to GPU, if you have GPU acceleration available
)

# Simple inference example
output = llm(
  "{prompt}", # Prompt
  max_tokens=512,  # Generate up to 512 tokens
  stop=["</s>"],   # Example stop token - not necessarily correct for this specific model! Please check before using.
  echo=True        # Whether to echo the prompt
)
```

#### Simple example code to load one of these GGUF models

```python
from ctransformers import AutoModelForCausalLM

# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("infosys/NT-Java-1.1B-GGUF", model_file="NT-Java-1.1B_Q4_K_M.gguf", model_type="gpt_bigcode", gpu_layers=50)

print(llm("public class HelloWorld {\n    public static void main(String[] args) {"))
```

## How to use with LangChain

Here are guides on using llama-cpp-python and ctransformers with LangChain:

* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)

<!-- README_GGUF.md-how-to-run end -->

<!-- footer start -->
<!-- 200823 -->

# Citation
```
@article{li2023starcoder,
      title={NARROW TRANSFORMER: STARCODER-BASED JAVA-LM FOR DESKTOP},
      author={Kamalkumar Rathinasamy and Balaji A J and Rajab Ali Mondal and Ankush Kumar and Harshini K and Gagan Gayari and Sreenivasa Raghavan Karumboor Seshadri},
      year={2024},
      eprint={2305.06161},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

#### Original model card: Infosys's [NT-Java-1.1B](https://huggingface.co/infosys/NT-Java-1.1B)

# **NT-Java**

The Narrow Transformer (NT) model NT-Java-1.1B is an open-source specialized code model built by extending pre-training on StarCoderBase-1B, designed for coding tasks in Java programming. The model is a decoder-only transformer with Multi-Query Attention and with a context length of 8192 tokens. The model was trained with Java subset of the StarCoderData dataset, which is ~22B tokens.