README.md

---
inference: false
license: other
model_creator: nRuaif
model_link: https://huggingface.co/nRuaif/Kimiko_13B
model_name: Kimiko 13B
model_type: llama
quantized_by: TheBloke
---

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# Kimiko 13B - GPTQ
- Model creator: [nRuaif](https://huggingface.co/nRuaif)
- Original model: [Kimiko 13B](https://huggingface.co/nRuaif/Kimiko_13B)

## Description

This repo contains GPTQ model files for [nRuaif's Kimiko 13B](https://huggingface.co/nRuaif/Kimiko_13B).

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.

## Repositories available

* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Kimiko-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/Kimiko-13B-GGML)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Kimiko-13B-fp16)
* [nRuaif's original LoRA adapter, which can be merged on to the base model.](https://huggingface.co/nRuaif/Kimiko_13B)

## Prompt template: Kimiko

```
<<HUMAN>>
{prompt}

<<AIBOT>>
```

## Provided files

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.

| Branch | Bits | Group Size | Act Order (desc_act) | File Size | ExLlama Compatible? | Made With | Description |
| ------ | ---- | ---------- | -------------------- | --------- | ------------------- | --------- | ----------- |
| [main](https://huggingface.co/TheBloke/Kimiko-13B-GPTQ/tree/main) | 4 | 128 | False | 7.26 GB | True | AutoGPTQ | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. | 
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Kimiko-13B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | True | 8.00 GB | True | AutoGPTQ | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. | 
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Kimiko-13B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | True | 7.51 GB | True | AutoGPTQ | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. | 
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Kimiko-13B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | True | 7.26 GB | True | AutoGPTQ | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. | 
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Kimiko-13B-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | True | 13.36 GB | False | AutoGPTQ | 8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed. | 
| [gptq-8bit-128g-actorder_False](https://huggingface.co/TheBloke/Kimiko-13B-GPTQ/tree/gptq-8bit-128g-actorder_False) | 8 | 128 | False | 13.65 GB | False | AutoGPTQ | 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. | 
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Kimiko-13B-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | True | 13.65 GB | False | AutoGPTQ | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. Poor AutoGPTQ CUDA speed. | 
| [gptq-8bit-64g-actorder_True](https://huggingface.co/TheBloke/Kimiko-13B-GPTQ/tree/gptq-8bit-64g-actorder_True) | 8 | 64 | True | 13.95 GB | False | AutoGPTQ | 8-bit, with group size 64g and Act Order for maximum inference quality. Poor AutoGPTQ CUDA speed. |

## How to download from branches

- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Kimiko-13B-GPTQ:gptq-4bit-32g-actorder_True`
- With Git, you can clone a branch with:
```
git clone --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Kimiko-13B-GPTQ`
```
- In Python Transformers code, the branch is the `revision` parameter; see below.

## 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 know how to make a manual install.

1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Kimiko-13B-GPTQ`.
  - To download from a specific branch, enter for example `TheBloke/Kimiko-13B-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: `Kimiko-13B-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 set 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!

## How to use this GPTQ model from Python code

First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) installed:

`GITHUB_ACTIONS=true pip install auto-gptq`

Then try the following example code:

```python
from transformers import AutoTokenizer, pipeline, logging
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig

model_name_or_path = "TheBloke/Kimiko-13B-GPTQ"
model_basename = "gptq_model-4bit-128g"

use_triton = False

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

model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
        model_basename=model_basename,
        use_safetensors=True,
        trust_remote_code=False,
        device="cuda:0",
        use_triton=use_triton,
        quantize_config=None)

"""
To download from a specific branch, use the revision parameter, as in this example:

model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
        revision="gptq-4bit-32g-actorder_True",
        model_basename=model_basename,
        use_safetensors=True,
        trust_remote_code=False,
        device="cuda:0",
        quantize_config=None)
"""

prompt = "Tell me about AI"
prompt_template=f'''<<HUMAN>>
{prompt}

<<AIBOT>>
'''

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

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

# Inference can also be done using transformers' pipeline

# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
logging.set_verbosity(logging.CRITICAL)

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.95,
    repetition_penalty=1.15
)

print(pipe(prompt_template)[0]['generated_text'])
```

## Compatibility

The files provided will work with AutoGPTQ (CUDA and Triton modes), GPTQ-for-LLaMa (only CUDA has been tested), and Occ4m's GPTQ-for-LLaMa fork.

ExLlama works with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.

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## 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!

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**: Luke from CarbonQuill, Aemon Algiz.

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Thank you to all my generous patrons and donaters!

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# Original model card: nRuaif's Kimiko 13B


# Model Card for Kimiko_13B

<!-- Provide a quick summary of what the model is/does. -->

This is my new Kimiko models, trained with LLaMA2-13B for...purpose

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** nRuaif
- **Model type:** Decoder only
- **License:**  CC BY-NC-SA 
- **Finetuned from model [optional]:** LLaMA 2

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/OpenAccess-AI-Collective/axolotl
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->


### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

This model is trained on 3k examples of instructions dataset, high quality roleplay, for best result follow this format
```
<<HUMAN>>
How to do abc

<<AIBOT>>
Here is how

Or with system prompting for roleplay

<<SYSTEM>>
A's Persona:
B's Persona:
Scenario:
Add some instruction here on how you want your RP to go.
```


## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

All bias of this model come from LlaMA2 with an exception of NSFW bias.....




## Training Details

### Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

3000 examples from LIMAERP, LIMA and I sample 1000 good instruction from Airboro

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

Model is trained with 1 L4 from GCP costing a whooping 2.5USD 





#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

3 epochs with 0.0002 lr, full 4096 ctx token, QLoRA

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

It takes 18 hours to train this model with xformers enable

[More Information Needed]







[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** L4 with 12CPUs 48gb ram
- **Hours used:** 5
- **Cloud Provider:** GCP
- **Compute Region:** US
- **Carbon Emitted:** 0.5KG