README.md

---
base_model: giux78/zefiro-7b-beta-ITA-v0.1
inference: false
license: apache-2.0
model_creator: giux78
model_name: zefiro-7b-beta-ITA-v0.1
model_type: mistral / zephyr
pipeline_tag: text-generation
prompt_template: '{prompt}
  '
quantized_by: giux78
tags:
- pretrained
---
<img src="https://hoodie-creator.s3.eu-west-1.amazonaws.com/15be78c6-original.png" alt="llamantino53" border="0" width="400px">

# Zefiro 7B v0.1 - GGUF
- Model creator: [giux78](https://huggingface.co/giux78)
- Base model: [Mistral 7B v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- Model ispiration [Zephyr_7B_v.0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)

<!-- description start -->
## Description

This repo contains GGUF format model files for [Zefiro-7B-beta-ITA](https://huggingface.co/giux78/zefiro-7b-beta-ITA-v0.1).

<!-- 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.
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:

```
<|assistant|>\nSei un assistente disponibile, rispettoso e onesto. Rispondi sempre nel modo piu' utile possibile, pur essendo sicuro. Le risposte non devono includere contenuti dannosi, non etici, razzisti, sessisti, tossici, pericolosi o illegali. Assicurati che le tue risposte siano socialmente imparziali e positive. Se una domanda non ha senso o non e' coerente con i fatti, spiegane il motivo invece di rispondere in modo non corretto. Se non conosci la risposta a una domanda, non condividere informazioni false.</s>\n<|user|>\nCrea una lista su cosa mangiare a pranzo ogni giorno della settimana a pranzo e cena</s>\n<|assistant|>
```

<!-- 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.
Sequence length note: The model will work at sequence lengths of 4096, or lower. GGUF does not yet have support for the new sliding window sequence length mode, so longer sequence lengths are not supported.
## 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [mistral-7b-v0.1.Q4_0.gguf](https://huggingface.co/giux78/zefiro-7b-beta-ITA-v0.1-GGUF/blob/main/zefiro-7b-beta-ITA-v0.1-q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss |
| [zefiro-7b-v0.1.Q8_0.gguf](https://huggingface.co/giux78/zefiro-7b-beta-ITA-v0.1-GGUF/blob/main/zeffiro-7b-beta-ITA-v0.1.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 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/Mistral-7B-v0.1-GGUF and below it, a specific filename to download, such as: mistral-7b-v0.1.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/Mistral-7B-v0.1-GGUF mistral-7b-v0.1.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 giux78/zefiro-7b-beta-ITA-v0.1-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 giux78/zefiro-7b-beta-ITA-v0.1-GGUF zefiro-7b-beta-ITA-v0.1-q4_0.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 zefiro-7b-beta-ITA-v0.1-q4_0.gguf --color -c 4096 --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.

Sequence length can be 4096 or lower. Mistral's sliding window sequence length is not yet supported in llama.cpp, so sequence lengths longer than 4096 are not supported.

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

Note: I have not tested ctransformers with Mistral models, but it may work if you set the `model_type` to `llama`.

#### 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("giux78/zefiro-7b-beta-ITA-v0.1-GGUF", model_file="zefiro-7b-beta-ITA-v0.1-q4_0.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 -->
<!-- footer start -->
<!-- 200823 -->

## Thanks, and how to contribute
Thanks to the [Business Operating System](https://www.businessos.xyz) team!

# Original model card: Zefiro-7b-beta-ITA-v0.1
# Model Card for Zefiro-7b-beta-ITA-v0.1
[Zefiro-7b-beta-ITA-v0.1](https://huggingface.co/giux78/zefiro-7b-beta-ITA-v0.1)

Special thanks to the team behind [Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)