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# Merlyn Education Corpus QA v2 - GGUF
- Model creator: [Merlyn Mind](https://huggingface.co/MerlynMind)
- Original model: [Merlyn Education Corpus QA v2](https://huggingface.co/MerlynMind/merlyn-education-corpus-qa-v2)

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

This repo contains GGUF format model files for [Merlyn Mind's Merlyn Education Corpus QA v2](https://huggingface.co/MerlynMind/merlyn-education-corpus-qa-v2).

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

<!-- 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 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.

<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available

* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/merlyn-education-corpus-qa-v2-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/merlyn-education-corpus-qa-v2-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/merlyn-education-corpus-qa-v2-GGUF)
* [Merlyn Mind's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/MerlynMind/merlyn-education-corpus-qa-v2)
<!-- repositories-available end -->

<!-- prompt-template start -->
## Prompt template: Merlyn-Education

```
Instruction:\t{system_message}
Conversation:
'user1':\tuser message to analyse
'user2':\tuser message to analyse
Response:

```

<!-- prompt-template end -->
<!-- licensing start -->
## Licensing

The creator of the source model has listed its license as `apache-2.0`, and this quantization has therefore used that same license.

As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.

In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Merlyn Mind's Merlyn Education Corpus QA v2](https://huggingface.co/MerlynMind/merlyn-education-corpus-qa-v2).
<!-- licensing 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [merlyn-education-corpus-qa-v2.Q2_K.gguf](https://huggingface.co/TheBloke/merlyn-education-corpus-qa-v2-GGUF/blob/main/merlyn-education-corpus-qa-v2.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes |
| [merlyn-education-corpus-qa-v2.Q3_K_S.gguf](https://huggingface.co/TheBloke/merlyn-education-corpus-qa-v2-GGUF/blob/main/merlyn-education-corpus-qa-v2.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss |
| [merlyn-education-corpus-qa-v2.Q3_K_M.gguf](https://huggingface.co/TheBloke/merlyn-education-corpus-qa-v2-GGUF/blob/main/merlyn-education-corpus-qa-v2.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss |
| [merlyn-education-corpus-qa-v2.Q3_K_L.gguf](https://huggingface.co/TheBloke/merlyn-education-corpus-qa-v2-GGUF/blob/main/merlyn-education-corpus-qa-v2.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss |
| [merlyn-education-corpus-qa-v2.Q4_0.gguf](https://huggingface.co/TheBloke/merlyn-education-corpus-qa-v2-GGUF/blob/main/merlyn-education-corpus-qa-v2.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [merlyn-education-corpus-qa-v2.Q4_K_S.gguf](https://huggingface.co/TheBloke/merlyn-education-corpus-qa-v2-GGUF/blob/main/merlyn-education-corpus-qa-v2.Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss |
| [merlyn-education-corpus-qa-v2.Q4_K_M.gguf](https://huggingface.co/TheBloke/merlyn-education-corpus-qa-v2-GGUF/blob/main/merlyn-education-corpus-qa-v2.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended |
| [merlyn-education-corpus-qa-v2.Q5_0.gguf](https://huggingface.co/TheBloke/merlyn-education-corpus-qa-v2-GGUF/blob/main/merlyn-education-corpus-qa-v2.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [merlyn-education-corpus-qa-v2.Q5_K_S.gguf](https://huggingface.co/TheBloke/merlyn-education-corpus-qa-v2-GGUF/blob/main/merlyn-education-corpus-qa-v2.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended |
| [merlyn-education-corpus-qa-v2.Q5_K_M.gguf](https://huggingface.co/TheBloke/merlyn-education-corpus-qa-v2-GGUF/blob/main/merlyn-education-corpus-qa-v2.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended |
| [merlyn-education-corpus-qa-v2.Q6_K.gguf](https://huggingface.co/TheBloke/merlyn-education-corpus-qa-v2-GGUF/blob/main/merlyn-education-corpus-qa-v2.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss |
| [merlyn-education-corpus-qa-v2.Q8_0.gguf](https://huggingface.co/TheBloke/merlyn-education-corpus-qa-v2-GGUF/blob/main/merlyn-education-corpus-qa-v2.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 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/merlyn-education-corpus-qa-v2-GGUF and below it, a specific filename to download, such as: merlyn-education-corpus-qa-v2.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/merlyn-education-corpus-qa-v2-GGUF merlyn-education-corpus-qa-v2.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/merlyn-education-corpus-qa-v2-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/merlyn-education-corpus-qa-v2-GGUF merlyn-education-corpus-qa-v2.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 merlyn-education-corpus-qa-v2.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Instruction:\t{system_message}\nConversation:\n'user1':\tuser message to analyse\n'user2':\tuser message to analyse\nResponse:"
```

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/merlyn-education-corpus-qa-v2-GGUF", model_file="merlyn-education-corpus-qa-v2.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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For further support, and discussions on these models and AI in general, join us at:

[TheBloke AI's Discord server](https://discord.gg/theblokeai)

## 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.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

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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 start -->
# Original model card: Merlyn Mind's Merlyn Education Corpus QA v2


# Merlyn-Education Corpus QA

merlyn-education-corpus-qa-v2 is a 13b parameter decoder-style transformer model for the education domain. It is fine-tuned from a [llama2-13b](https://huggingface.co/meta-llama/Llama-2-13b-hf) base-model.

This model was trained by [Merlyn Mind](https://www.merlyn.org/).

It is a model that provides an answer to a question based on the given context.

## Model Date

August 21, 2023

## Model License

Apache-2.0


## Usage

Loading model and tokenizer:

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_path = "MerlynMind/merlyn-education-corpus-qa-v2"
device = torch.device("cuda:0") # change device id as necessary
model = AutoModelForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path, fast_tokenizer=True)
model.to(device) # move to device

```

Prompt example:

```python
info = '''Information:\tThe Solar System is about 4.6 billion years old. The Sun formed by gravity in a large molecular cloud. It is mainly hydrogen, which it converts into helium.
Information:\tThe formation and evolution of the Solar System began 4.6 billion years ago with the gravitational collapse of a small part of a giant molecular cloud.
Information:\tAstronomers are now more or less certain that the order of the planets was not always as it is today. Knowing what we know today, we can see the Solar System is strange. All other planetary system we are able to study have their largest planet close to their star. Also we have noticed other oddities in the Solar System. Mars is smaller than it ought to be, and the asteroid belt has been disturbed.
Information:\tFor thousands of years, people had no need for a name for the "Solar System". They thought the Earth stayed still at the center of everything (geocentrism). The Greek philosopher Aristarchus of Samos suggested that there was a special order in the sky. Nicolaus Copernicus was the first to develop a mathematical system that described what we now call the "Solar System". This was called a "new system of the world". In the 17th century, Galileo Galilei, Johannes Kepler and Isaac Newton began to understand physics more clearly. People began to accept the idea that the Earth is a planet that moves around the Sun, and that the planets are worlds, and that all worlds are governed by the same same physical laws. More recently, telescopes and space probes sometimes let us see details directly. All inner planets have surface features. The gas giants (as the name suggests) have surfaces whose make-up is gradually being discovered.
Information:\tThere are eight planets in the Solar System. From closest to farthest from the Sun, they are: Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus and Neptune. The first four planets are called terrestrial planets. They are mostly made of rock and metal, and they are mostly solid. The last four planets are called gas giants. This is because they are much larger than other planets and are mostly made of gas.
'''
qs = "Question:\tHow old is the Solar System?"

prompt = tokenizer.bos_token
prompt += '''Instruction:\tYou are to try to answer the following question using only the pieces of information given.
Instruction:\tYour response should be a well formed JSON object with an 'answerable' property followed by an 'answer' property.
Instruction:\tIf you cannot answer the question given the information, the value of the 'answerable' should be 'false' and the 'answer' should be an empty string.
Instruction:\tIf you can answer the question given the information, the value of the 'answerable' should be 'true' and your answer should be the string value of the 'answer' property.
''' + info + qs + " Response:"

```

We recommend using newline character for stopping criterion, as follows:

```python
from transformers import StoppingCriteria, StoppingCriteriaList

eos_tokens = [tokenizer.eos_token,'\n']
eos_token_ids = [tokenizer.encode(token)[0] for token in eos_tokens]

class MultipleEOSTokensStoppingCriteria(StoppingCriteria):
    def __init__(self, eos_token_ids):
        self.eos_token_ids = set(eos_token_ids)
    def __call__(self, input_ids, scores) -> bool:
        if input_ids.shape[-1] <= 1:
            return False
        for eos_token_id in self.eos_token_ids:
            if eos_token_id == input_ids[0, -1].item():
                return True
        return False

# Define stopping criteria
multiple_eos_tokens_processor = MultipleEOSTokensStoppingCriteria(eos_token_ids)
stopping_criteria = StoppingCriteriaList([multiple_eos_tokens_processor])
```

Inference:

```python
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False).to(device)
generate_ids = model.generate(
    **inputs,
    max_new_tokens=1024,
    temperature=0.0,
    num_beams=2,
    top_p=1,
    stopping_criteria=stopping_criteria
)
response = tokenizer.decode(generate_ids[0],
                      skip_special_tokens=True,
                      clean_up_tokenization_spaces=True)
```

Example output (after response processing):

```json
[{"answerable": "true", "answer": "4.6 billion years"}]
```

## Evaluation
This model is trained on a larger dataset compared to the [pythia-based v1 model](https://huggingface.co/MerlynMind/merlyn-education-corpus-qa), yielding better correctness and reduced hallucinations on a larger and more diverse benchmarking dataset.



<!-- original-model-card end -->

{"license": "apache-2.0", "tags": ["MerlynMind", "education"], "model_name": "Merlyn Education Corpus QA v2", "base_model": "MerlynMind/merlyn-education-corpus-qa-v2", "inference": false, "model_creator": "Merlyn Mind", "model_type": "llama", "prompt_template": "Instruction:\\t{system_message}\nConversation:\n'user1':\\tuser message to analyse\n'user2':\\tuser message to analyse\nResponse:\n", "quantized_by": "TheBloke"}
transformers gguf llama MerlynMind education base_model:MerlynMind/merlyn-education-corpus-qa-v2 license:apache-2.0 text-generation-inference region:us