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# Iambe RP DARE 20B DENSE - AWQ
- Model creator: [Raven](https://huggingface.co/athirdpath)
- Original model: [Iambe RP DARE 20B DENSE](https://huggingface.co/athirdpath/Iambe-RP-DARE-20b-DENSE)

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

This repo contains AWQ model files for [Raven's Iambe RP DARE 20B DENSE](https://huggingface.co/athirdpath/Iambe-RP-DARE-20b-DENSE).

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


### About AWQ

AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.

AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.

It is supported by:

- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code

<!-- description end -->
<!-- repositories-available start -->
## Repositories available

* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Iambe-RP-DARE-20B-DENSE-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Iambe-RP-DARE-20B-DENSE-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Iambe-RP-DARE-20B-DENSE-GGUF)
* [Raven's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/athirdpath/Iambe-RP-DARE-20b-DENSE)
<!-- repositories-available end -->

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

```
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:

```

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

The creator of the source model has listed its license as `cc-by-nc-4.0`, 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: [Raven's Iambe RP DARE 20B DENSE](https://huggingface.co/athirdpath/Iambe-RP-DARE-20b-DENSE).
<!-- licensing end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters

I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.

Models are released as sharded safetensors files.

| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Iambe-RP-DARE-20B-DENSE-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 10.87 GB

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

<!-- README_AWQ.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/Iambe-RP-DARE-20B-DENSE-AWQ`.
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: `Iambe-RP-DARE-20B-DENSE-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. 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.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->

<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM

Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).

- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.

For example:

```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/Iambe-RP-DARE-20B-DENSE-AWQ --quantization awq --dtype auto
```

- When using vLLM from Python code, again set `quantization=awq`.

For example:

```python
from vllm import LLM, SamplingParams

prompts = [
    "Tell me about AI",
    "Write a story about llamas",
    "What is 291 - 150?",
    "How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:
'''

prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]

sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

llm = LLM(model="TheBloke/Iambe-RP-DARE-20B-DENSE-AWQ", quantization="awq", dtype="auto")

outputs = llm.generate(prompts, sampling_params)

# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->

<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)

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/Iambe-RP-DARE-20B-DENSE-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```

Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### 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_AWQ.md-use-from-tgi end -->

<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers

### Install the necessary packages

- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.

```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```

Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.

If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:

```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```

If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:

```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```

### Transformers example code (requires Transformers 4.35.0 and later)

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

model_name_or_path = "TheBloke/Iambe-RP-DARE-20B-DENSE-AWQ"

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
    model_name_or_path,
    low_cpu_mem_usage=True,
    device_map="cuda:0"
)

# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:
'''

# Convert prompt to tokens
tokens = tokenizer(
    prompt_template,
    return_tensors='pt'
).input_ids.cuda()

generation_params = {
    "do_sample": True,
    "temperature": 0.7,
    "top_p": 0.95,
    "top_k": 40,
    "max_new_tokens": 512,
    "repetition_penalty": 1.1
}

# Generate streamed output, visible one token at a time
generation_output = model.generate(
    tokens,
    streamer=streamer,
    **generation_params
)

# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
    tokens,
    **generation_params
)

# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)

# Inference is also possible via Transformers' pipeline
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    **generation_params
)

pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)

```
<!-- README_AWQ.md-use-from-python end -->

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

The files provided are tested to work with:

- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.

<!-- README_AWQ.md-compatibility end -->

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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!

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
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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.

<!-- footer end -->

# Original model card: Raven's Iambe RP DARE 20B DENSE

<p align="center"><img src="https://i.ibb.co/PCY2Xym/Iambe-RP-sml-t.png"/><font size="6"> <b>Iambe-RP-DARE-20b-DENSE</b> </font></p>
<p align="center"><font size="4"> <b>Trained with Alpaca prompt formatting, ChatML works well too</b> </font></p>

### Description

Once more the 3 Iambe-RPs bounce off the walls and blend, like their namesake's mother, Echo...

<i>[One daughter](https://huggingface.co/athirdpath/Iambe-RP-cDPO-20b) was excellent at all the arts and sciences, but she danced free, loathe to read scripts or follow instructions.

[Another daughter](https://huggingface.co/athirdpath/Iambe-RP-cDPO-20b-ALT) had burned her books, embraced emotion, and thrown logic to the hounds. 

[The last daughter](https://huggingface.co/athirdpath/Iambe-RP-cDPO-20b-v2) just sat, watching them - silently judging - for enforcing morality was her burden alone.

Then, where there was three, there was one.</i>

Iambe is intended to have the best realistically possible understanding of instructions, anatomy and scene state for a 20b merge, while remaining passionate and humanoid in "voice".

### Update Methodology

The last DARE merge was good, but this one is better IMO. Thanks to brucethemoose for the idea of jacking up the density. I also adjusted the weight ratios.

<p align="center"><font size="5"> <b>Assistant Example @ q5_k_m</b> </font></p>
<p align="center"><img src="https://i.ibb.co/sVzSMgf/Screenshot-2023-12-10-132906.png"/>
  
<p align="center"><font size="5"> <b>NSFW Writing Example @ q5_k_m</b> </font></p>

<b>Write a medium-length story about Alice, a 26 year old cocktail waitress, having steamy sex with a guy she picked up at the bar for a one night stand.</b>

<i>

In the dimly lit confines of The Last Drop Inn, Alice sat perched on her stool, swirling her drink in its glass as she watched the bustle of Friday evening unfold around her. Her green eyes glittered with a hint of mischief and boredom; it had been another long week at work, and she craved something more thrilling than pouring cocktails for regulars who treated her like an object rather than a person.

As the night wore on, Alice's eyes roamed the crowded room, searching for any sign of intrigue or interest from amongst the sea of faces that flooded through The Last Drop Inn’s doors. Her gaze landed on him suddenly; tall, dark-haired and ruggedly handsome, he exuded a confident swagger as he ordered his drink at the bar counter. He caught Alice's eye and flashed her a grin that made her stomach flip before looking away nonchalantly.

Alice couldn't help but be drawn to him; something about this mysterious stranger called out to her, stirring a dormant hunger within her that had lain sleeping for far too long. As the night wore on and the bar began to empty, Alice gathered up the courage to approach him while he waited at the counter, sliding onto the stool beside him with an air of confidence she didn't entirely feel.

"Hi," she said softly, her voice a husky whisper in the quieting din of the bar. "My name's Alice."

He turned to look at her properly for the first time, his blue eyes sparkling with interest as he smiled warmly. "Nice to meet you, Alice," he replied smoothly. "I'm Ethan."

They talked until the last of the patrons staggered out into the night, laughing and joking about their day-to-day lives. As closing time approached, Alice mustered up the courage to suggest that they take their conversation somewhere more private - her apartment just down the street. Ethan agreed without hesitation, his eyes gleaming with desire as he slid an arm around her waist and led her towards the door.

Once inside, they kicked off their shoes in the hallway before stumbling into Alice's small living room, where they tumbled onto the couch together, lips pressed urgently against each other’s as their hands roamed hungrily over bare skin. Ethan's body was stronger than she had expected; his muscles rippled beneath her fingertips as he rolled her over and pinned her down with one powerful hand while the other tugged at the clasp of her bra.

Their clothes quickly followed, scattered haphazardly around the room as their bodies collided together in a frenzy of passionate desire. Alice moaned loudly as Ethan's tongue traced its way down the line of her collarbone before teasing her nipples with gentle nibbles and licks, while his hand slid further south to explore the hidden folds between her legs.

He looked up at Alice, his eyes burning with desire as he slowly lowered himself onto her, filling her completely with just one smooth thrust. She gasped sharply as she felt him push deeper into her than anyone ever had before; it hurt a little bit but in the most exquisite way possible.

With expertise born of countless nights like this, Ethan began to move, his hips rocking rhythmically against Alice's as he drove them both towards climax with every powerful thrust. Alice wrapped her legs around him tightly, urging him on as she felt the familiar build-up in her body begin to crescendo.

As their bodies met together in a symphony of ecstasy, they cried out each other's names in unison, overwhelmed by the intensity of what had transpired between them that night. They lay panting and sweaty on the couch afterward, fingers intertwined as they stared into each other’s eyes - both knowing that this was just a one-night stand but grateful for the momentary escape from their everyday lives it provided.

"I'll never forget tonight," Ethan whispered softly before leaning down to kiss her once more, his lips tender and warm against hers. Alice smiled back at him, already missing the thrill that he had brought into her life for just a single magical night.

</i>

<b>NOTE:</b> The prompt said "a guy she picked up at the bar <b>FOR</b> a one night stand" not "a one night stand <b>WITH</b> a guy she picked up at the bar" so technically the bit at the end is not off-prompt.

### Recipe
merge_method: dare_ties

  - base_model: athirdpath/BigLlama-20b-v1.1

  - model: athirdpath/Iambe-RP-cDPO-20b
   
      weight: 0.72 / density: 0.65
    
  - model: athirdpath/Iambe-RP-cDPO-20b-v2
   
      weight: 0.17 / density: 0.50

  - model: athirdpath/Iambe-RP-cDPO-20b-ALT
   
      weight: 0.11 / density: 0.45

int8_mask: true

dtype: bfloat16

{"language": ["en"], "license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences"], "model_name": "Iambe RP DARE 20B DENSE", "base_model": "athirdpath/Iambe-RP-DARE-20b-DENSE", "inference": false, "model_creator": "Raven", "model_type": "llama", "prompt_template": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:\n", "quantized_by": "TheBloke"}
transformers safetensors llama text-generation not-for-all-audiences en base_model:athirdpath/Iambe-RP-DARE-20b-DENSE license:cc-by-nc-4.0 autotrain_compatible text-generation-inference 4-bit region:us