README.md

---
base_model: MerlynMind/merlyn-education-corpus-qa-v2
inference: false
license: apache-2.0
model_creator: Merlyn Mind
model_name: Merlyn Education Corpus QA v2
model_type: llama
prompt_template: 'Instruction:\t{system_message}

  Conversation:

  ''user1'':\tuser message to analyse

  ''user2'':\tuser message to analyse

  Response:

  '
quantized_by: TheBloke
tags:
- MerlynMind
- education
---
<!-- markdownlint-disable MD041 -->

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# Merlyn Education Corpus QA v2 - GPTQ
- 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 GPTQ model files for [Merlyn Mind's Merlyn Education Corpus QA v2](https://huggingface.co/MerlynMind/merlyn-education-corpus-qa-v2).

Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.

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

<!-- description 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 -->

<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers

These GPTQ models are known to work in the following inference servers/webuis.

- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)

This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->

<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters

Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.

Each separate quant is in a different branch.  See below for instructions on fetching from different branches.

Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.

<details>
  <summary>Explanation of GPTQ parameters</summary>

- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.

</details>

| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/merlyn-education-corpus-qa-v2-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 7.26 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | 
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/merlyn-education-corpus-qa-v2-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 8.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | 
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/merlyn-education-corpus-qa-v2-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. | 
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/merlyn-education-corpus-qa-v2-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. | 
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/merlyn-education-corpus-qa-v2-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 14.54 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. | 
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/merlyn-education-corpus-qa-v2-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |

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

<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches

### In text-generation-webui

To download from the `main` branch, enter `TheBloke/merlyn-education-corpus-qa-v2-GPTQ` in the "Download model" box.

To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/merlyn-education-corpus-qa-v2-GPTQ:gptq-4bit-32g-actorder_True`

### From the command line

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

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

To download the `main` branch to a folder called `merlyn-education-corpus-qa-v2-GPTQ`:

```shell
mkdir merlyn-education-corpus-qa-v2-GPTQ
huggingface-cli download TheBloke/merlyn-education-corpus-qa-v2-GPTQ --local-dir merlyn-education-corpus-qa-v2-GPTQ --local-dir-use-symlinks False
```

To download from a different branch, add the `--revision` parameter:

```shell
mkdir merlyn-education-corpus-qa-v2-GPTQ
huggingface-cli download TheBloke/merlyn-education-corpus-qa-v2-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir merlyn-education-corpus-qa-v2-GPTQ --local-dir-use-symlinks False
```

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

If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.

The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.

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
mkdir merlyn-education-corpus-qa-v2-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/merlyn-education-corpus-qa-v2-GPTQ --local-dir merlyn-education-corpus-qa-v2-GPTQ --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>

### With `git` (**not** recommended)

To clone a specific branch with `git`, use a command like this:

```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/merlyn-education-corpus-qa-v2-GPTQ
```

Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)

<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)

Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).

It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.

1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/merlyn-education-corpus-qa-v2-GPTQ`.

    - To download from a specific branch, enter for example `TheBloke/merlyn-education-corpus-qa-v2-GPTQ:gptq-4bit-32g-actorder_True`
    - see Provided Files above for the list of branches for each option.

3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `merlyn-education-corpus-qa-v2-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.

    - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.

9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!

<!-- README_GPTQ.md-text-generation-webui end -->

<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)

It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`

Example Docker parameters:

```shell
--model-id TheBloke/merlyn-education-corpus-qa-v2-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```

Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):

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

```python
from huggingface_hub import InferenceClient

endpoint_url = "https://your-endpoint-url-here"

prompt = "Tell me about AI"
prompt_template=f'''Instruction:\t{system_message}
Conversation:
'user1':\tuser message to analyse
'user2':\tuser message to analyse
Response:
'''

client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
                                  max_new_tokens=128,
                                  do_sample=True,
                                  temperature=0.7,
                                  top_p=0.95,
                                  top_k=40,
                                  repetition_penalty=1.1)

print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## Python code example: inference from this GPTQ model

### Install the necessary packages

Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.

```shell
pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```

If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:

```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```

### Example Python code

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

model_name_or_path = "TheBloke/merlyn-education-corpus-qa-v2-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                             device_map="auto",
                                             trust_remote_code=False,
                                             revision="main")

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

prompt = "Tell me about AI"
prompt_template=f'''Instruction:\t{system_message}
Conversation:
'user1':\tuser message to analyse
'user2':\tuser message to analyse
Response:
'''

print("\n\n*** Generate:")

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))

# Inference can also be done using transformers' pipeline

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    top_k=40,
    repetition_penalty=1.1
)

print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->

<!-- README_GPTQ.md-compatibility start -->
## Compatibility

The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.

[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.

For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility end -->

<!-- footer start -->
<!-- 200823 -->
## Discord

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.

* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI

**Special thanks to**: Aemon Algiz.

**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius


Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

<!-- footer end -->

# 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.