README.md

---
base_model: Xwin-LM/XwinCoder-34B
inference: false
license: llama2
model_creator: Xwin-LM
model_name: XwinCoder 34B
model_type: llama
prompt_template: "<system>: You are an AI coding assistant that helps people with\
  \ programming. Write a response that appropriately completes the user's request.\n\
  <user>: {prompt}\n<AI>: \n"
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->

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# XwinCoder 34B - GGUF
- Model creator: [Xwin-LM](https://huggingface.co/Xwin-LM)
- Original model: [XwinCoder 34B](https://huggingface.co/Xwin-LM/XwinCoder-34B)

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## Description

This repo contains GGUF format model files for [Xwin-LM's XwinCoder 34B](https://huggingface.co/Xwin-LM/XwinCoder-34B).

These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).

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<!-- 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.

Here is an incomplete 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.

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## Repositories available

* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/XwinCoder-34B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/XwinCoder-34B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/XwinCoder-34B-GGUF)
* [Xwin-LM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Xwin-LM/XwinCoder-34B)
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<!-- prompt-template start -->
## Prompt template: XWin-Coder

```
<system>: You are an AI coding assistant that helps people with programming. Write a response that appropriately completes the user's request.
<user>: {prompt}
<AI>: 

```

<!-- prompt-template end -->


<!-- compatibility_gguf start -->
## Compatibility

These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)

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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [xwincoder-34b.Q2_K.gguf](https://huggingface.co/TheBloke/XwinCoder-34B-GGUF/blob/main/xwincoder-34b.Q2_K.gguf) | Q2_K | 2 | 14.21 GB| 16.71 GB | smallest, significant quality loss - not recommended for most purposes |
| [xwincoder-34b.Q3_K_S.gguf](https://huggingface.co/TheBloke/XwinCoder-34B-GGUF/blob/main/xwincoder-34b.Q3_K_S.gguf) | Q3_K_S | 3 | 14.61 GB| 17.11 GB | very small, high quality loss |
| [xwincoder-34b.Q3_K_M.gguf](https://huggingface.co/TheBloke/XwinCoder-34B-GGUF/blob/main/xwincoder-34b.Q3_K_M.gguf) | Q3_K_M | 3 | 16.28 GB| 18.78 GB | very small, high quality loss |
| [xwincoder-34b.Q3_K_L.gguf](https://huggingface.co/TheBloke/XwinCoder-34B-GGUF/blob/main/xwincoder-34b.Q3_K_L.gguf) | Q3_K_L | 3 | 17.77 GB| 20.27 GB | small, substantial quality loss |
| [xwincoder-34b.Q4_0.gguf](https://huggingface.co/TheBloke/XwinCoder-34B-GGUF/blob/main/xwincoder-34b.Q4_0.gguf) | Q4_0 | 4 | 19.05 GB| 21.55 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [xwincoder-34b.Q4_K_S.gguf](https://huggingface.co/TheBloke/XwinCoder-34B-GGUF/blob/main/xwincoder-34b.Q4_K_S.gguf) | Q4_K_S | 4 | 19.15 GB| 21.65 GB | small, greater quality loss |
| [xwincoder-34b.Q4_K_M.gguf](https://huggingface.co/TheBloke/XwinCoder-34B-GGUF/blob/main/xwincoder-34b.Q4_K_M.gguf) | Q4_K_M | 4 | 20.22 GB| 22.72 GB | medium, balanced quality - recommended |
| [xwincoder-34b.Q5_0.gguf](https://huggingface.co/TheBloke/XwinCoder-34B-GGUF/blob/main/xwincoder-34b.Q5_0.gguf) | Q5_0 | 5 | 23.24 GB| 25.74 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [xwincoder-34b.Q5_K_S.gguf](https://huggingface.co/TheBloke/XwinCoder-34B-GGUF/blob/main/xwincoder-34b.Q5_K_S.gguf) | Q5_K_S | 5 | 23.24 GB| 25.74 GB | large, low quality loss - recommended |
| [xwincoder-34b.Q5_K_M.gguf](https://huggingface.co/TheBloke/XwinCoder-34B-GGUF/blob/main/xwincoder-34b.Q5_K_M.gguf) | Q5_K_M | 5 | 23.84 GB| 26.34 GB | large, very low quality loss - recommended |
| [xwincoder-34b.Q6_K.gguf](https://huggingface.co/TheBloke/XwinCoder-34B-GGUF/blob/main/xwincoder-34b.Q6_K.gguf) | Q6_K | 6 | 27.68 GB| 30.18 GB | very large, extremely low quality loss |
| [xwincoder-34b.Q8_0.gguf](https://huggingface.co/TheBloke/XwinCoder-34B-GGUF/blob/main/xwincoder-34b.Q8_0.gguf) | Q8_0 | 8 | 35.86 GB| 38.36 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 -->

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## 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: TheBloke/XwinCoder-34B-GGUF and below it, a specific filename to download, such as: xwincoder-34b.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 TheBloke/XwinCoder-34B-GGUF xwincoder-34b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```

<details>
  <summary>More advanced huggingface-cli download usage</summary>

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

```shell
huggingface-cli download TheBloke/XwinCoder-34B-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 TheBloke/XwinCoder-34B-GGUF xwincoder-34b.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 32 -m xwincoder-34b.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<system>: You are an AI coding assistant that helps people with programming. Write a response that appropriately completes the user's request.\n<user>: {prompt}\n<AI>:"
```

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

Change `-c 4096` 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.

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 can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%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) or [ctransformers](https://github.com/marella/ctransformers) libraries.

### How to load this model in Python code, using ctransformers

#### First install the package

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

```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```

#### Simple ctransformers example code

```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("TheBloke/XwinCoder-34B-GGUF", model_file="xwincoder-34b.Q4_K_M.gguf", model_type="llama", gpu_layers=50)

print(llm("AI is going to"))
```

## 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)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)

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

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## Thanks, and how to contribute

Thanks to the [chirper.ai](https://chirper.ai) team!

Thanks to Clay from [gpus.llm-utils.org](llm-utils)!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

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**Special thanks to**: Aemon Algiz.

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Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

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# Original model card: Xwin-LM's XwinCoder 34B

# XwinCoder

We are glad to introduce our instruction finetuned code generation models based on CodeLLaMA: XwinCoder. We release model weights and evaluation code.

**Repository:** [https://github.com/Xwin-LM/Xwin-LM/tree/main/Xwin-Coder](https://github.com/Xwin-LM/Xwin-LM/tree/main/Xwin-Coder)

**Models:**
| Model | 🤗hf link | HumanEval pass@1 | MBPP pass@1 | APPS-intro pass@5 |
|-------|------------|----------|------|-------------|
| XwinCoder-7B | [link](https://huggingface.co/Xwin-LM/XwinCoder-7B) | 63.8 | 57.4 | 31.5 |
| XwinCoder-13B | [link](https://huggingface.co/Xwin-LM/XwinCoder-13B) | 68.8 | 60.1 | 35.4 |
| XwinCoder-34B | [link](https://huggingface.co/Xwin-LM/XwinCoder-34B) | 74.2 | 64.8 | 43.0 |

## Updates
- 💥 We released  [**XwinCoder-7B**](https://huggingface.co/Xwin-LM/XwinCoder-7B), [**XwinCoder-13B**](https://huggingface.co/Xwin-LM/XwinCoder-13B), [**XwinCoder-34B**](https://huggingface.co/Xwin-LM/XwinCoder-34B). Our XwinCoder-34B reached 74.2 on HumanEval and it **achieves comparable performance as GPT-3.5-turbo on 6 benchmarks**.

- We support evaluating instruction finetuned models on HumanEval, MBPP, APPS, DS1000 and MT-Bench. See our github repository.

## Overview

![Chat demo](rader.png)

* To fully demonstrate our model's coding capabilities in real-world usage scenarios, we have conducted thorough evaluations on several existing mainstream coding capability leaderboards (rather than only on the currently most popular HumanEval).
* As shown in the radar chart results, our 34B model **achieves comparable performance as GPT-3.5-turbo on coding abilities**.
* It is worth mentioning that, to ensure accurate visualization, our radar chart has not been scaled (only translated; MT-Bench score is scaled by 10x to be more comparable with other benchmarks).
* Multiple-E-avg6 refer to the 6 languages used in CodeLLaMA paper. Results of GPT-4 and GPT-3.5-turbo are conducted by us, more details will be released later.

## Demo
We provide a chat demo in our github repository, here are some examples:
![Chat demo](exm.gif)




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