---
base_model: crumb/Gale-medium-init
inference: false
model_creator: Maxine
model_name: Gale Medium Init 3B
model_type: mistral
prompt_template: '{prompt}

  '
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->

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# Gale Medium Init 3B - GGUF
- Model creator: [Maxine](https://huggingface.co/crumb)
- Original model: [Gale Medium Init 3B](https://huggingface.co/crumb/Gale-medium-init)

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## Description

This repo contains GGUF format model files for [Maxine's Gale Medium Init 3B](https://huggingface.co/crumb/Gale-medium-init).

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 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 -->
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## Repositories available

* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Gale-medium-init-3B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Gale-medium-init-3B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Gale-medium-init-3B-GGUF)
* [Maxine's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/crumb/Gale-medium-init)
<!-- repositories-available end -->

<!-- prompt-template start -->
## Prompt template: Unknown

```
{prompt}

```

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<!-- 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [gale-medium-init-3B.Q2_K.gguf](https://huggingface.co/TheBloke/Gale-medium-init-3B-GGUF/blob/main/gale-medium-init-3B.Q2_K.gguf) | Q2_K | 2 | 1.34 GB| 3.84 GB | smallest, significant quality loss - not recommended for most purposes |
| [gale-medium-init-3B.Q3_K_S.gguf](https://huggingface.co/TheBloke/Gale-medium-init-3B-GGUF/blob/main/gale-medium-init-3B.Q3_K_S.gguf) | Q3_K_S | 3 | 1.38 GB| 3.88 GB | very small, high quality loss |
| [gale-medium-init-3B.Q3_K_M.gguf](https://huggingface.co/TheBloke/Gale-medium-init-3B-GGUF/blob/main/gale-medium-init-3B.Q3_K_M.gguf) | Q3_K_M | 3 | 1.54 GB| 4.04 GB | very small, high quality loss |
| [gale-medium-init-3B.Q3_K_L.gguf](https://huggingface.co/TheBloke/Gale-medium-init-3B-GGUF/blob/main/gale-medium-init-3B.Q3_K_L.gguf) | Q3_K_L | 3 | 1.65 GB| 4.15 GB | small, substantial quality loss |
| [gale-medium-init-3B.Q4_0.gguf](https://huggingface.co/TheBloke/Gale-medium-init-3B-GGUF/blob/main/gale-medium-init-3B.Q4_0.gguf) | Q4_0 | 4 | 1.78 GB| 4.28 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [gale-medium-init-3B.Q4_K_S.gguf](https://huggingface.co/TheBloke/Gale-medium-init-3B-GGUF/blob/main/gale-medium-init-3B.Q4_K_S.gguf) | Q4_K_S | 4 | 1.81 GB| 4.31 GB | small, greater quality loss |
| [gale-medium-init-3B.Q4_K_M.gguf](https://huggingface.co/TheBloke/Gale-medium-init-3B-GGUF/blob/main/gale-medium-init-3B.Q4_K_M.gguf) | Q4_K_M | 4 | 1.87 GB| 4.37 GB | medium, balanced quality - recommended |
| [gale-medium-init-3B.Q5_0.gguf](https://huggingface.co/TheBloke/Gale-medium-init-3B-GGUF/blob/main/gale-medium-init-3B.Q5_0.gguf) | Q5_0 | 5 | 2.15 GB| 4.65 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [gale-medium-init-3B.Q5_K_S.gguf](https://huggingface.co/TheBloke/Gale-medium-init-3B-GGUF/blob/main/gale-medium-init-3B.Q5_K_S.gguf) | Q5_K_S | 5 | 2.15 GB| 4.65 GB | large, low quality loss - recommended |
| [gale-medium-init-3B.Q5_K_M.gguf](https://huggingface.co/TheBloke/Gale-medium-init-3B-GGUF/blob/main/gale-medium-init-3B.Q5_K_M.gguf) | Q5_K_M | 5 | 2.20 GB| 4.70 GB | large, very low quality loss - recommended |
| [gale-medium-init-3B.Q6_K.gguf](https://huggingface.co/TheBloke/Gale-medium-init-3B-GGUF/blob/main/gale-medium-init-3B.Q6_K.gguf) | Q6_K | 6 | 2.54 GB| 5.04 GB | very large, extremely low quality loss |
| [gale-medium-init-3B.Q8_0.gguf](https://huggingface.co/TheBloke/Gale-medium-init-3B-GGUF/blob/main/gale-medium-init-3B.Q8_0.gguf) | Q8_0 | 8 | 3.29 GB| 5.79 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: TheBloke/Gale-medium-init-3B-GGUF and below it, a specific filename to download, such as: gale-medium-init-3B.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/Gale-medium-init-3B-GGUF gale-medium-init-3B.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/Gale-medium-init-3B-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/Gale-medium-init-3B-GGUF gale-medium-init-3B.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 gale-medium-init-3B.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```

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.

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/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).

## 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/Gale-medium-init-3B-GGUF", model_file="gale-medium-init-3B.Q4_K_M.gguf", model_type="mistral", 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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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: Maxine's Gale Medium Init 3B

No original model card was available.

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